0419.ipynb

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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nrVRW-4QrfxB"
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      "outputs": [],
      "source": [
        "# 라이브러리 불러오기\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "np.set_printoptions(precision=4, suppress=True)\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 다항회귀분석 - 릿지 L2"
      ],
      "metadata": {
        "id": "tGsN7coqeEae"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "id": "WiqPwuhaeEaf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dc10f130-f30f-44a0-e59a-a9cbdcf28353"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['mpg', 'cylinders', 'displacement', 'horsepower', 'weight',\n",
              "       'acceleration', 'model_year', 'origin', 'car_name'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 전체 데이터\n",
        "X = df.loc[:, ['cylinders', 'horsepower', 'weight']]\n",
        "y = df['mpg']\n",
        "\n",
        "print(\"X 데이터: \", X.shape)\n",
        "print(\"y 데이터: \", y.shape)"
      ],
      "metadata": {
        "id": "tkQpzWOueEaf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c9de7453-a216-4aae-834e-3414e48266ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X 데이터:  (392, 3)\n",
            "y 데이터:  (392,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터 분할 = 훈련 데이터 / 테스트 데이터 = 7:3\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
        "                                                                      test_size = 0.3,\n",
        "                                                                      random_state = 42)\n",
        "print(\"train 데이터: \", X_train.shape)\n",
        "print(\"test 데이터: \", X_test.shape)"
      ],
      "metadata": {
        "id": "q2CgVHYCeEag",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a1475ffc-efa4-4567-cba7-3a1c16b4d2e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train 데이터:  (274, 3)\n",
            "test 데이터:  (118, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "# 다항식\n",
        "poly = PolynomialFeatures(degree = 5, include_bias = False)\n",
        "X_train_poly = poly.fit_transform(X_train)\n",
        "X_test_poly = poly.transform(X_test)\n",
        "\n",
        "print(\"X_train 데이터: \", X_train.shape)\n",
        "print(\"X_train_poly 데이터: \", X_train_poly.shape)"
      ],
      "metadata": {
        "id": "dWYWQpcheEaf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "156c5312-f0b1-4a7f-bc19-a2e06b97cbbd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train 데이터:  (274, 3)\n",
            "X_train_poly 데이터:  (274, 55)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 표준화\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "standard = StandardScaler()\n",
        "X_train_scale = standard.fit_transform(X_train_poly)\n",
        "X_test_scale = standard.transform(X_test_poly)"
      ],
      "metadata": {
        "id": "VM7x2F4FiOUe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 릿지\n",
        "from sklearn.linear_model import Ridge\n",
        "ridge = Ridge()\n",
        "# 모델 훈련\n",
        "ridge.fit(X_train_scale, y_train)"
      ],
      "metadata": {
        "id": "wZO0Jqq1eEag",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "outputId": "d5c5848f-a69a-4b56-87e8-abd933ebf99d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ridge()"
            ],
            "text/html": [
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            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "# 모델 평가\n",
        "# 적합도 측도 = 결정계수 = 설명력\n",
        "print(ridge.score(X_train_scale, y_train))\n",
        "print(ridge.score(X_test_scale, y_test))"
      ],
      "metadata": {
        "id": "xwgSPa8-eEag",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d5a30a92-0263-446d-a97b-5b230cbdf002"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7763873563181758\n",
            "0.6833550248045738\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 계수 = 모델 파라미터\n",
        "# 절편\n",
        "intercept = ridge.intercept_;intercept"
      ],
      "metadata": {
        "id": "P1K_nU1BeEag",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a7ea04ec-8672-4865-874c-7b5f93badde7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "23.637591240875913"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 기울기\n",
        "slope = [round(x, 4) for x in ridge.coef_];print(slope)"
      ],
      "metadata": {
        "id": "bzMwJMJzeEag",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c81889e2-56e2-42c7-a5a1-533d39d6af7e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-0.8878, -6.3843, -2.4368, -1.7734, 0.2135, -1.0436, -0.5709, -1.853, -1.3697, -1.2949, 0.4679, -0.6121, 1.4977, 0.5964, -0.4372, 1.2961, 0.5156, -0.3162, -0.5862, -0.0917, -0.0063, 0.3247, 0.4584, 0.6282, 0.3052, 0.8148, 0.8857, 0.6101, 0.0612, 0.9118, 0.7835, 0.4265, 0.0482, -0.2111, 1.3761, -0.3652, 1.5854, -1.0306, 0.3635, 1.3276, -1.0485, -0.1373, 0.5472, 0.8109, -0.691, -0.2341, 0.1137, 0.3221, 0.2377, -0.1092, -0.0873, -0.1611, -0.1944, -0.1821, -0.2269]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(slope)"
      ],
      "metadata": {
        "id": "Btzjn-T7j8C3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7ebd7e4a-81b8-4328-e231-1d9ff7ce7129"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "55"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_poly_columns = poly.get_feature_names_out()"
      ],
      "metadata": {
        "id": "iRgBaCbIeEag"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for name, coef in zip(X_poly_columns, slope):\n",
        "  print(\"{}: {}\".format(name, coef))"
      ],
      "metadata": {
        "id": "fsWtkODdeEag",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3bc7edb7-fda3-4f7c-e581-b22d821c1c01"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cylinders: -0.8878\n",
            "horsepower: -6.3843\n",
            "weight: -2.4368\n",
            "cylinders^2: -1.7734\n",
            "cylinders horsepower: 0.2135\n",
            "cylinders weight: -1.0436\n",
            "horsepower^2: -0.5709\n",
            "horsepower weight: -1.853\n",
            "weight^2: -1.3697\n",
            "cylinders^3: -1.2949\n",
            "cylinders^2 horsepower: 0.4679\n",
            "cylinders^2 weight: -0.6121\n",
            "cylinders horsepower^2: 1.4977\n",
            "cylinders horsepower weight: 0.5964\n",
            "cylinders weight^2: -0.4372\n",
            "horsepower^3: 1.2961\n",
            "horsepower^2 weight: 0.5156\n",
            "horsepower weight^2: -0.3162\n",
            "weight^3: -0.5862\n",
            "cylinders^4: -0.0917\n",
            "cylinders^3 horsepower: -0.0063\n",
            "cylinders^3 weight: 0.3247\n",
            "cylinders^2 horsepower^2: 0.4584\n",
            "cylinders^2 horsepower weight: 0.6282\n",
            "cylinders^2 weight^2: 0.3052\n",
            "cylinders horsepower^3: 0.8148\n",
            "cylinders horsepower^2 weight: 0.8857\n",
            "cylinders horsepower weight^2: 0.6101\n",
            "cylinders weight^3: 0.0612\n",
            "horsepower^4: 0.9118\n",
            "horsepower^3 weight: 0.7835\n",
            "horsepower^2 weight^2: 0.4265\n",
            "horsepower weight^3: 0.0482\n",
            "weight^4: -0.2111\n",
            "cylinders^5: 1.3761\n",
            "cylinders^4 horsepower: -0.3652\n",
            "cylinders^4 weight: 1.5854\n",
            "cylinders^3 horsepower^2: -1.0306\n",
            "cylinders^3 horsepower weight: 0.3635\n",
            "cylinders^3 weight^2: 1.3276\n",
            "cylinders^2 horsepower^3: -1.0485\n",
            "cylinders^2 horsepower^2 weight: -0.1373\n",
            "cylinders^2 horsepower weight^2: 0.5472\n",
            "cylinders^2 weight^3: 0.8109\n",
            "cylinders horsepower^4: -0.691\n",
            "cylinders horsepower^3 weight: -0.2341\n",
            "cylinders horsepower^2 weight^2: 0.1137\n",
            "cylinders horsepower weight^3: 0.3221\n",
            "cylinders weight^4: 0.2377\n",
            "horsepower^5: -0.1092\n",
            "horsepower^4 weight: -0.0873\n",
            "horsepower^3 weight^2: -0.1611\n",
            "horsepower^2 weight^3: -0.1944\n",
            "horsepower weight^4: -0.1821\n",
            "weight^5: -0.2269\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 표본회귀식\n",
        "equation = \"y_hat = \" + str(intercept)\n",
        "for coef, name in zip(slope, X_poly_columns):\n",
        "  equation += \" + {} * {}\".format(coef, name)\n",
        "print(equation)"
      ],
      "metadata": {
        "id": "a7UITwepeEag",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c3aeaf74-4b8d-46c0-8f63-8bfc8f58c1c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "y_hat = 23.637591240875913 + -0.8878 * cylinders + -6.3843 * horsepower + -2.4368 * weight + -1.7734 * cylinders^2 + 0.2135 * cylinders horsepower + -1.0436 * cylinders weight + -0.5709 * horsepower^2 + -1.853 * horsepower weight + -1.3697 * weight^2 + -1.2949 * cylinders^3 + 0.4679 * cylinders^2 horsepower + -0.6121 * cylinders^2 weight + 1.4977 * cylinders horsepower^2 + 0.5964 * cylinders horsepower weight + -0.4372 * cylinders weight^2 + 1.2961 * horsepower^3 + 0.5156 * horsepower^2 weight + -0.3162 * horsepower weight^2 + -0.5862 * weight^3 + -0.0917 * cylinders^4 + -0.0063 * cylinders^3 horsepower + 0.3247 * cylinders^3 weight + 0.4584 * cylinders^2 horsepower^2 + 0.6282 * cylinders^2 horsepower weight + 0.3052 * cylinders^2 weight^2 + 0.8148 * cylinders horsepower^3 + 0.8857 * cylinders horsepower^2 weight + 0.6101 * cylinders horsepower weight^2 + 0.0612 * cylinders weight^3 + 0.9118 * horsepower^4 + 0.7835 * horsepower^3 weight + 0.4265 * horsepower^2 weight^2 + 0.0482 * horsepower weight^3 + -0.2111 * weight^4 + 1.3761 * cylinders^5 + -0.3652 * cylinders^4 horsepower + 1.5854 * cylinders^4 weight + -1.0306 * cylinders^3 horsepower^2 + 0.3635 * cylinders^3 horsepower weight + 1.3276 * cylinders^3 weight^2 + -1.0485 * cylinders^2 horsepower^3 + -0.1373 * cylinders^2 horsepower^2 weight + 0.5472 * cylinders^2 horsepower weight^2 + 0.8109 * cylinders^2 weight^3 + -0.691 * cylinders horsepower^4 + -0.2341 * cylinders horsepower^3 weight + 0.1137 * cylinders horsepower^2 weight^2 + 0.3221 * cylinders horsepower weight^3 + 0.2377 * cylinders weight^4 + -0.1092 * horsepower^5 + -0.0873 * horsepower^4 weight + -0.1611 * horsepower^3 weight^2 + -0.1944 * horsepower^2 weight^3 + -0.1821 * horsepower weight^4 + -0.2269 * weight^5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train_pred = ridge.predict(X_train_scale)\n",
        "y_test_pred = ridge.predict(X_test_scale)"
      ],
      "metadata": {
        "id": "ZrhmwwPxeEag"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 예측 성능 평가\n",
        "# RMSE, MAE\n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
        "train_rmse = mean_squared_error(y_train, y_train_pred, squared = False)\n",
        "test_rmse = mean_squared_error(y_test, y_test_pred, squared = False)\n",
        "print(\"train RMSE: \", train_rmse)\n",
        "print(\"test RMSE: \", test_rmse)\n",
        "train_mae = mean_absolute_error(y_train, y_train_pred)\n",
        "test_mae = mean_absolute_error(y_test, y_test_pred)\n",
        "print(\"train MAE: \", train_mae)\n",
        "print(\"test MAE: \", test_mae)"
      ],
      "metadata": {
        "id": "MTsarT_xeEag",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "757ef333-5caa-4520-ec7d-beb976a7a81c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train RMSE:  3.7837474384323713\n",
            "test RMSE:  4.092926644427514\n",
            "train MAE:  2.747583776577178\n",
            "test MAE:  3.0593186509931694\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 실제값과 예측값 비교\n",
        "plt.figure(figsize = (5, 3))\n",
        "ax1 = sns.kdeplot(y, label = \"y\")\n",
        "ax2 = sns.kdeplot(y_train_pred, label = \"train y_hat\", ax = ax1)\n",
        "ax3 = sns.kdeplot(y_test_pred, label = \"test y_hat\", ax = ax1)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "ZVGPwyVIeEag",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "outputId": "395b4563-4f7e-49ec-9f97-b59cff74df70"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 규제조절 - 결정계수\n",
        "train_score = []\n",
        "test_score = []\n",
        "alpha_list = [0.0001, 0.001, 0.01, 0.1, 1, 10, 100]\n",
        "for alpha in alpha_list:\n",
        "  ridge = Ridge(alpha = alpha)\n",
        "  ridge.fit(X_train_scale, y_train)\n",
        "  train_score.append(ridge.score(X_train_scale, y_train))\n",
        "  test_score.append(ridge.score(X_test_scale, y_test))"
      ],
      "metadata": {
        "id": "QRakZRc_ksCH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(alpha_list, train_score)\n",
        "plt.plot(alpha_list, test_score)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "N6QbvR20rj9y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "020e72bd-b9ff-4d23-9c86-57d0c7ff6df2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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ZMaFakRqjFbOtSp8erdBAPy+1FAAwVhFYMKzsDkMfnG7Qvp7el/dPNbgtnfa3mLQoOUrLeybvzpkSToFGAMCgCCwYUY3nulR4tM41/+X02XNu16NDA7Qs1dozgdeq2PAgL7UUAODLCCwYNYZh6GRdW094sanwqE2tnXa3e66MD9OK2c7wsjglSkH+VJkGABBYvN2cCa3L7lDJybPOvV/KavVRRaMu/Lcs0M+s9BnRWtEzgXd2HKUDAGCiIrDAZ9S3duqvR5wb1+0rs7mqTPeKCw90rTxaNsuqKEoHAMCEQWCBTzIMQ0dqWvRWT3jZf7xO7V3upQOuTojQitnO3pfrpk1WgB+lAwBgvCKwYExo77LrvRNnta+sVnvLbPq0ssnturN0QLSr9tF0ayjDRwAwjhBYMCbVNLWroGf4qOCITbYW99IBUycHO4ePUq1aMsuqiGBKBwDAWEZgwZjncBj6tKpJe0udm9e9d+KsOu3nh4/MJmlBUmTP6qMYzZ8aIT8qTwPAmEJgwbjT1tmt/cfqtbfMOf/lSE2L2/XwID8tndVbOsCqqZNDvNRSAMBQEVgw7lU0nFNBz94vBUdsajzX5XZ9hjVUy1OtWjE7RtfPoHQAAPgiAgsmFLvD0EcVjT1Lp2tVUn5x6YDrpk3WitkxWpEao7kJlA4AAF8w1J/flzXgv23bNqWkpCgoKEjp6ekqKioa8N4bb7xRJpPpomPVqlVu93366ae6+eabFRERodDQUC1evFjl5eWX0zxMQBazSQuSIvXA51O1694lOrDlb/SfdyzU7ddP07SoEHXZDe0/Xq+nXzusm54r0KKtf9YDLx3QrvdOqaqxffA/AADgVR73sOzcuVPr1q3T9u3blZ6ertzcXO3atUuHDx9WbGzsRffX19ers/P8So+6ujrNnz9f//3f/61vfOMbkqSjR48qLS1N3/rWt3TrrbcqPDxcH3/8sa6//vp+n9kfelhwKSfrWrW3zLn6qPBonVo63CtPXxEXpuWpVi2fHaP06ZQOAIDRMmJDQunp6Vq8eLGee+45SZLD4VBSUpLuv/9+bdq0adDP5+bmasuWLaqsrFRoaKgkae3atfL399f//u//etIUNwQWDFWX3aH3TzVob6lz75cPTze4lQ4I8DMrfXqUs3DjbKuuiAtj7xcAGCEjElg6OzsVEhKi3/zmN1q9erXrfFZWlhoaGvTKK68M+ox58+YpIyNDzz//vCRn4ImIiNDDDz+sgoICHThwQNOnT9fmzZvd/oy+Ojo61NHR4frnpqYmJSUlEVjgsbOtnfrrUZv2lTprH1X2GSKKDQt0rTxaOssq66RAL7UUAMafoQYWj5ZN2Gw22e12xcXFuZ2Pi4vToUOHBv18UVGRDh48qLy8PNe5mpoatbS06KmnntIPfvAD/ehHP9KePXv01a9+VW+++aZuuOGGfp+Vk5OjJ554wpPmA/2aHBqgL1+ToC9fkyDDMHS0tkV7e8LLO8fqVNPcod+WnNZvS05Lkq5ODO/ZvC5GC5MpHQAAo8GjHpYzZ84oMTFRb7/9tjIyMlznH374Yb311lvav3//JT9/zz33qLCwUB9++OFFz7z11lv14osvus7ffPPNCg0N1UsvvdTvs+hhwWjo6HaWDthbVqt9pTZ90qd0QEiARdf3Vp6eHaMZlA4AAI+MSA+L1WqVxWJRdXW12/nq6mrFx8df8rOtra3asWOHnnzyyYue6efnpzlz5ridv+qqq1RQUDDg8wIDAxUYSNc8Rlagn0VLZzmHgjb/rVTT3K6/HukdPrLJ1tKhNw7V6I1DNZKkxMhgV+HGpTOtigihdAAADAePAktAQIAWLlyo/Px81/wSh8Oh/Px8bdiw4ZKf3bVrlzo6OnT77bdf9MzFixfr8OHDbudLS0uVnJzsSfOAERcbFqSvXDtVX7l2qhwOQ4eqmnsKN9bq3eNnVdFwTi8VndJLRadkNknzkyK1PDVGN8y2av7USEoHAMBl8njrz+zsbGVlZWnRokVKS0tTbm6uWltbtX79eknSunXrlJiYqJycHLfP5eXlafXq1YqOjr7omQ899JDWrFmjFStW6HOf+5z27Nmj3//+9/rLX/5yed8KGAVms0lzEsI1JyFc99wwU+c67XrneJ329dQ+Kqtp0YHyBh0ob9BP8ssUFuinJbOiXZvXJUVROgAAhsrjwLJmzRrV1tZqy5Ytqqqq0oIFC7Rnzx7XRNzy8nKZze7/FXn48GEVFBTo9ddf7/eZX/nKV7R9+3bl5OTogQce0BVXXKHf/va3WrZs2WV8JcA7ggMs+twVsfrcFc69g840nFNBmXPybsERmxrauvTax9V67WPnkGpKdIircGPGzGhNonQAAAyIrfmBUWB3GDpY0egcPiq1qaT8rLovKB3gZzbpuuTJWtFT++jqhAhKBwCYEKglBPiw5vYuFR6t074y5/DRibo2t+uTQ/y1dJa1pwfGqikRwV5qKQCMLAILMIaU17U5l06X1ertI3Vq7lM6IDV2kiu8pE+PVnAApQMAjA8EFmCM6rI79MGpBlftow9PN8jRp3RAWkqUs/ZRaoyumkLpAABjF4EFGCca2jr11yN1PfNfanWmT+mAmLBALe8ZPlo6y6qYMPYnAjB2EFiAcchZOqBV+8pqta/MpsKjdTrXZXe7Z86U8J6l01YtTJmsQD+GjwD4LgILMAF0dNtVfPKs9vbs/fLxGffSAcH+Fl0/I6qneGOMZsZQOgCAbyGwABOQraXDtffLvjKbaps73K4nRAS5wsvSWdGKDAnwUksBwInAAkxwhnG+dMC+Mpv2H69XZ7fDdd1kkq6ZGqkbego3LkiKlD+lAwCMMgILADfnOu0qOlGvvaXO5dOl1S1u18MC/ZQxM1rLZ8fohtQYTYumdACAkUdgAXBJVY3trqGjgrJanW3rcrueHB2i5alWregpHRAWROVpAMOPwAJgyOwOQx+fadS+nr1fik+6lw6wmE26blqkVqTGaPnsGM1LjJCF0gEAhgGBBcBla+no1jtH61w9MMdtrW7XI3tLB/RsXpcQSekAAJeHwAJg2Jyq7ykdUGrTX4/a1NzuXjpgVuwk5/DR7BilT49SSACVpwEMDYEFwIjotjv0wekG7S11Lp/+4FSf0gEWsxalTHbVProqPpzK0wAGRGABMCoa27r09lFneNlbalNFwzm369ZJgT11j6xalmpVbFiQl1oKwBcRWACMOsMwdNzW2rN02qbCY3Vq63QvHXDVlHCt6Bk+Wpg8WUH+lA4AJjICCwCv6+x2qPjkWWfhxrJaHaxwLx0Q5G9W+vRoV+2jWbGTKB0ATDAEFgA+p66lQwVHbK7aRzV9SgdMiQjqGT6K0bJZVk0OpXQAMN4RWAD4NMMwVFrdor2lzt6XouP16uhbOiAxwlX76NpplA4AxiMCC4Axpb3LrqLj9c7ho1KbDlc3u12fFOin62dE64bZzh6YFGuol1oKYDgRWACMadVN7a6ddwuO2FTf2ul2fVpUiGv4aMmsaIVTOgAYkwgsAMYNh8PQJ5VNequncGPxybPqsruXDrg2KbJn+Miqa6ZGUjoAGCMILADGrdaObr1zrM7VA3OsT+mAiGB/LZ0V7ap9lEjpAMBnEVgATBin6tt6Vh85h4/6lg6YGRPq6n1Jnx6t0EBKBwC+gsACYEJylg5o1L6ewo0Hys+6lQ7wt5i0KDlKy2dbtSI1RnOmUDoA8CYCCwBIajzXpcKjNu3tGT46fda9dEB0aICWpTrDy/JUq2LDKR0AjCYCCwD0YRiGTtS19SydrlXh0Tq19ikdcGV8mKtw4+KUKEoHACOMwAIAg+jsdqik/Kxr+OijikZd+DdioJ9Z6TOiXbWPUikdAAw7AgsAeKi+tVMFR2za17P7bnWTe+mAuPBA1867y2ZZFUXpAOAzI7AAwGdgGIbKanpLB9i0/1jdRaUDrk6I0IqenXevmzZZAX6UDgA8RWABgGHU3mXXuyfqXXu/HKpyLx0QGmBRxsxoVw9MSnQIw0fAEBBYAGAE1fSUDuid/1LXp3TA1MnBWp4aoxtmW5Ux06qIYEoHAP0hsADAKOktHbC3rFb7Sm1672S9W+kAs0lakBTZs/ooRvOnRsiPytOAJAKLt5sDYAJr7ejW/uN12lvq7IE5WuteOiA8yE9LZ1ldu+9OnRzipZYC3kdgAQAfcfpsmwrKbNpXZlPBEZsaz3W5XZ9hDdXynqXT18+gdAAmFgILAPggu8PQh6cbXL0vB041yH5B7QB/i0nXTZusFbNjtCI1RnMTKB2A8Y3AAgBjQFN7l94+UufcfbesVqfq3UsHRIUGaNksq6sHJo7SARhnCCwAMAadsLVqX1mt3iq1qfCo7aLSAVfEhbnCS9p0Sgdg7COwAMAY12V36EB5g/aW1mpfWa0+7Kd0QNr0KGfhxtlWXREXxt4vGHOG+vP7stbVbdu2TSkpKQoKClJ6erqKiooGvPfGG2+UyWS66Fi1alW/9997770ymUzKzc29nKYBwLjhb3EGkn9eeYVe2bBMJd/7Gz1767X6h0VTFR8epI5uh/aV2bT1j5/qi7n7lP7DfP3Trz/QK+9XqK6lY/A/ABhDPJ6KvnPnTmVnZ2v79u1KT09Xbm6uVq5cqcOHDys2Nvai+3fv3q3OzvMbKtXV1Wn+/Pm65ZZbLrr35Zdf1jvvvKOEhARPmwUA497k0ADdND9BN81PkGEYOlLTor09O+/uP16nmuYO/bbktH5bclqSdHViuHPpdGqMFiZTOgBjm8dDQunp6Vq8eLGee+45SZLD4VBSUpLuv/9+bdq0adDP5+bmasuWLaqsrFRoaKjrfEVFhdLT0/Xaa69p1apV2rhxozZu3DjkdjEkBGAia++yq/jkWVfto08rm9yuhwRYlDEjWstTrVo+O0YzrKEMH8EnDPXnt0c9LJ2dnSouLtbmzZtd58xmszIzM1VYWDikZ+Tl5Wnt2rVuYcXhcOiOO+7QQw89pLlz53rSJACApCB/i5bOsmrpLKs2S6ppbnft/bKvrFa2lk7lH6pR/qEaSVJiZLCrcOPSmVZFhFA6AL7No8Bis9lkt9sVFxfndj4uLk6HDh0a9PNFRUU6ePCg8vLy3M7/6Ec/kp+fnx544IEht6Wjo0MdHefHaJuami5xNwBMLLFhQfrqdVP11eumyuEw9GlVk6tw43snzqqi4ZxeKjqll4pOyWyS5idFumofzZ8aSekA+JxR3U4xLy9P8+bNU1pamutccXGxfvzjH6ukpMSj7smcnBw98cQTI9FMABhXzGaT5iZEaG5ChO69YabaOru1/3h9z+ojm47UtOhAeYMOlDfoJ/llCgvy09KZVi2fbdWK1BglRVE6AN7n0RyWzs5OhYSE6De/+Y1Wr17tOp+VlaWGhga98sorA362tbVVCQkJevLJJ/Xggw+6zufm5io7O1tm8/k0b7fbZTablZSUpBMnTvT7vP56WJKSkpjDAgAeOtNwrmfjOpsKyi4uHTC9p3TA8tQYZcyM1iRKB2AYjdg+LOnp6UpLS9Ozzz4ryTn/ZNq0adqwYcMlJ93+4he/0L333quKigpFR0e7ztfV1amystLt3pUrV+qOO+7Q+vXrdcUVVwypXUy6BYDPzu4w9FFFo/aVOnfeLSl3Lx3gZzbpuuTJWtGzed3VCRGUDsBnMiKTbiUpOztbWVlZWrRokdLS0pSbm6vW1latX79ekrRu3TolJiYqJyfH7XN5eXlavXq1W1iRpOjo6IvO+fv7Kz4+fshhBQAwPCxmkxYkRWpBUqTu/3yqmtu7VHi0TnvLnMNHJ+vaVHS8XkXH6/Vvr5dqcoi/ls5yhpflqVZNiQj29lfAOOVxYFmzZo1qa2u1ZcsWVVVVacGCBdqzZ49rIm55ebnb8I4kHT58WAUFBXr99deHp9UAgFERFuSvL8yN1xfmxkuSTta1am+ZTftKa/X20TqdbevSHz6s1B8+dPaUz46bpOWpzvCSPj1awQGUDsDwYGt+AMBl6bI79P6pBu0rrdVbZTZ9eLrBrXRAgJ9ZaSlRrtpHV8ZTOgAXo5YQAGBUNbR16q9H6no2r6tVZWO72/WYsEAt7xk+WpZqlXVSoJdaCl9CYAEAeI1hGDpa2+oq3PjOsXqd63KvPD03obd0gFULUyYr0I/ho4mIwAIA8Bkd3XYVnzjrqn30SZ/SAcH+Fl0/I8oZYGbHaGYMpQMmCgILAMBn1TZ36K9HbK7aR7Y+1aUTIoJc4WXprGhFhgR4qaUYaQQWAMCYYBiGDlU1u3beLTpRr85uh+u62SRdMzVSK3oKNy5IipQ/pQPGDQILAGBMOtdp1/7jda7aR2U1LW7XwwL9lDEzWstnx+iG1BhNi6Z0wFhGYAEAjAuVjedc4aXgiE0Nbe6lA5KjQ7SiZ++XjJnRCgui8vRYQmABAIw7doehj880uua+lJw8q+6+pQOmTXbWPpodo3mJEbJQOsCnEVgAAONec3uX3jlW7yzeWFqrE3Vtbtcje0sH9BRvTIikdICvIbAAACac8ro27TviDC9vH6lTc0e32/VZsZOcw0ezrUqfHqWQACpPexuBBQAwoXX3lA7YW2bTvrJafXCqQY4LSwdYzFqUMtlVuPGq+HAqT3sBgQUAgAs0tHXq7aM9pQNKa3WmT+kA66RA59yXnuGjmDBKB4wGAgsAAAMwDEPHbK2uvV8Kj9ZdVDrgqinhWjHbqhWpMVqYPFlB/pQOGAkEFgAAhqij267ik2e1r2f46GCFe+mAIH+zrp8R7ap9NCt2EqUDhgmBBQCAy2RrcZYOeKunB6a22b10wJSIIC1PdVaeXjrTqsmhlA64XAQWAACGgWEYOlx9vnTA/uPupQNMJumaxIieybsxunYapQM8QWABAGAEtHfZtf94vfb1BJjD1c1u1yf1lA7o3fslxRrqpZaODQQWAABGQVVju/aVOcNLwRGb6ls73a5PiwpxrTxaMita4ZQOcENgAQBglDkchj4+06S9PTvvFvcpHWAxm3RtUqRz8u5sq66ZGjnhSwcQWAAA8LKWjm69c7TO1QNzzNbqdj0i2F/LZlldtY8SJ2DpAAILAAA+5lR9m2vpdMERm5rb3UsHzIwJdfW+XD8jekKUDiCwAADgw7rtDn1wurFn9VGt3u9TOsDfYtKi5ChX6YA5U8Zn6QACCwAAY0jjuS69fcSmvWU27S2tVUXDObfr1kkBPcNHzgATGx7kpZYOLwILAABjlGEYOm5rdQ0fvX20Tm2d7qUDrowPc/W+LE6JGrOlAwgsAACME53dDpWUn3VtXvdRRaPb9UA/s9JnOPd+WTE7RqljqHQAgQUAgHGqrqVDBUdsrh6Y6ib30gHx4UGulUfLZlkV5cOlAwgsAABMAIZhqLS6RfvKavVWaa2Kjtero0/pgHmJEa7N666bNlkBfr5TOoDAAgDABNTeZde7J+pdw0eHqtxLB4QGWJylA3pqH6VEh3h1+IjAAgAAVN3U7ho62ld2cemAqZODtWJ2jFakWpUx06qI4NEtHUBgAQAAbhwOQ59UupcO6LK7lw5YkBTpGj6aPzVCfiNceZrAAgAALqm1o1v7j9dpb6lNe8tqdazWvXRAeJCfls6yupZPT50cMuxtILAAAACPnD57QemAMpua+pQOeHXDUl0zNXJY/8yh/vwe/0UKAADAkEydHKJb06bp1rRp6rY79GFFo/a5el9aNGeK9zoE6GEBAACDau+yj8huukP9+e07C7EBAIDP8vbW/wQWAADg8wgsAADA5xFYAACAzyOwAAAAn0dgAQAAPu+yAsu2bduUkpKioKAgpaenq6ioaMB7b7zxRplMpouOVatWSZK6urr0yCOPaN68eQoNDVVCQoLWrVunM2fOXN43AgAA447HgWXnzp3Kzs7W448/rpKSEs2fP18rV65UTU1Nv/fv3r1blZWVruPgwYOyWCy65ZZbJEltbW0qKSnRY489ppKSEu3evVuHDx/WzTff/Nm+GQAAGDc83jguPT1dixcv1nPPPSdJcjgcSkpK0v33369NmzYN+vnc3Fxt2bJFlZWVCg0N7feed999V2lpaTp58qSmTZs2pHaxcRwAAGPPiGwc19nZqeLiYmVmZp5/gNmszMxMFRYWDukZeXl5Wrt27YBhRZIaGxtlMpkUGRk54D0dHR1qampyOwAAwPjkUWCx2Wyy2+2Ki4tzOx8XF6eqqqpBP19UVKSDBw/qzjvvHPCe9vZ2PfLII7r11lsvmbRycnIUERHhOpKSkob+RQAAwJgyqquE8vLyNG/ePKWlpfV7vaurS//wD/8gwzD005/+9JLP2rx5sxobG13HqVOnRqLJAADAB3hUrdlqtcpisai6utrtfHV1teLj4y/52dbWVu3YsUNPPvlkv9d7w8rJkyf1xhtvDDoPJTAwUIGBgZ40HwAAjFEe9bAEBARo4cKFys/Pd51zOBzKz89XRkbGJT+7a9cudXR06Pbbb7/oWm9YKSsr05///GdFR0d70iwAADDOedTDIknZ2dnKysrSokWLlJaWptzcXLW2tmr9+vWSpHXr1ikxMVE5OTlun8vLy9Pq1asvCiNdXV36+7//e5WUlOgPf/iD7Ha7az5MVFSUAgICLve7AQCAccLjwLJmzRrV1tZqy5Ytqqqq0oIFC7Rnzx7XRNzy8nKZze4dN4cPH1ZBQYFef/31i55XUVGhV199VZK0YMECt2tvvvmmbrzxRk+bCAAAxhmP92HxVezDAgDA2DMi+7AAAAB4A4EFAAD4PAILAADweQQWAADg8wgsAADA5xFYAACAzyOwAAAAn0dgAQAAPo/AAgAAfB6BBQAA+DwCCwAA8HkEFgAA4PMILAAAwOcRWAAAgM8jsAAAAJ9HYAEAAD6PwAIAAHwegQUAAPg8AgsAAPB5BBYAAODzCCwAAMDnEVgAAIDPI7AAAACfR2ABAAA+j8ACAAB8HoEFAAD4PAILAADweQQWAADg8wgsAADA5xFYAACAzyOwAAAAn0dgAQAAPo/AAgAAfB6BBQAA+DwCCwAA8HkEFgAA4PMILAAAwOcRWAAAgM8jsAAAAJ93WYFl27ZtSklJUVBQkNLT01VUVDTgvTfeeKNMJtNFx6pVq1z3GIahLVu2aMqUKQoODlZmZqbKysoup2kAAGAc8jiw7Ny5U9nZ2Xr88cdVUlKi+fPna+XKlaqpqen3/t27d6uystJ1HDx4UBaLRbfccovrnn/913/VT37yE23fvl379+9XaGioVq5cqfb29sv/ZgAAYNwwGYZhePKB9PR0LV68WM8995wkyeFwKCkpSffff782bdo06Odzc3O1ZcsWVVZWKjQ0VIZhKCEhQf/0T/+kf/7nf5YkNTY2Ki4uTr/4xS+0du3aIbWrqalJERERamxsVHh4uCdfCQAAeMlQf3571MPS2dmp4uJiZWZmnn+A2azMzEwVFhYO6Rl5eXlau3atQkNDJUnHjx9XVVWV2zMjIiKUnp5+yWd2dHSoqanJ7QAAAOOTR4HFZrPJbrcrLi7O7XxcXJyqqqoG/XxRUZEOHjyoO++803Wu93OePjMnJ0cRERGuIykpyZOvAgAAxpBRXSWUl5enefPmKS0t7TM/a/PmzWpsbHQdp06dGoYWAgAAX+RRYLFarbJYLKqurnY7X11drfj4+Et+trW1VTt27NC3vvUtt/O9n/P0mYGBgQoPD3c7AADA+ORRYAkICNDChQuVn5/vOudwOJSfn6+MjIxLfnbXrl3q6OjQ7bff7nZ++vTpio+Pd3tmU1OT9u/fP+gzR8Vrj0qvbJAaT3u7JQAATFh+nn4gOztbWVlZWrRokdLS0pSbm6vW1latX79ekrRu3TolJiYqJyfH7XN5eXlavXq1oqOj3c6bTCZt3LhRP/jBD5Samqrp06frscceU0JCglavXn3532y4fPQbqaVKSr9Hipjq7dYAADAheRxY1qxZo9raWm3ZskVVVVVasGCB9uzZ45o0W15eLrPZvePm8OHDKigo0Ouvv97vMx9++GG1trbq7rvvVkNDg5YtW6Y9e/YoKCjoMr4SAAAYbzzeh8VXjdg+LP92hbOH5d4CKX7e8D0XAACMzD4sAAAA3kBgAQAAPo/AAgAAfB6BBQAA+DwCCwAA8HkEFgAA4PMILAAAwOcRWAAAgM8jsAAAAJ9HYAEAAD6PwAIAAHwegQUAAPg8AgsAAPB5BBYAAODzCCwAAMDnEVgAAIDPI7AAAACfR2ABAAA+j8ACAAB8HoEFAAD4PALLYPyDnL92tnq3HQAATGAElsEEhjl/7WzxbjsAAJjACCyD8Q91/koPCwAAXkNgGUxAiPPXzjbvtgMAgAnMz9sN8Hn+PYGly8d7WBwOqfZT6USBdGKf1HhaCpsihSf0/Jro/H3vr71BDACAMYDAMpiASc5ffa2HxeGQaj45H1BO/lU6d7bPTQcG/nxQ5AUhJqH/3weFj+Q3AABgyAgsg3ENCXm5h8XhkGo+7gkoBf0HFP9QaVq6lLJMsl4htdZITWd6jorzv+9skdobnEfNxwP/mQGTLh1owhOl4MmSyTSS3xwAAALLoLw1JORwSNUH3QNKe0OftoVK0653BpSU5VLCAsniP/iz25suDjF9f9/e4Aw2tlLnMRC/oJ5hp4SBw01ojGRmuhQA4PIRWAYT0LtKaISHhBz2fgJKY5+2THIPKFPmDy2g9BUU7jxirxz4ns5WqanyfJBpPnNxuGmtlbrbpfpjzmMgZr8+gaY31Fwwt2ZSvGThX0cAQP/4CTGYwJ55HGdKJHv38P1Qddilqo/OB5TytwcIKBl9Asoo/U8WECpZZzmPgXR3SM2V/fTSVPSEnTNSS5Xk6JYay53HQExmaVJc/8NOvefCpkh+gcP/XQEAPs9kGIbh7UYMh6amJkVERKixsVHh4cM4WbThlPQfGVJns/S5R6UbHr685zjsUtWHF/SgFEodfQNKmJTcE1CSl41uQBkp9m6ppbqfQHNBj03zGWeoGYoQ66Xn1IRPOd8rBgDweUP9+U1gGYoPdkov3y2ZLNI3X5OSFg/+GXu3e0ApL5Q6mtzvCQiTkpdIKUudISV+HASUy+FwOIeX+ht2uvD33e1De57bCqgp/YebwHAmCwOADyCwDCfDkH57p3TwN9LkFOmefRcv+bV3S1UfXBBQ3rk4oASGOwNKcm9AuWZiBpTLYRjOVVH9DTtdeG6oJRQGWgEVdsG5kChCDQCMMALLcGtvlH66zDkPY/7XpZuflSo/OL8HyslC57DRhQIjzg/x9AYUs2X424bzBlsB1Xymn/1qBmAJvMTwEyugAGA4EFhGwslC6RdfkgyHc0lx36XOgRE9Qzy9AWUeAcUXdbb1TBbub07NBSughsLsd35H4YHCDSugAGBAQ/35zd+inkjOkJb/s7T3X51hJSji/PBOyjIp7moCylgQECJFz3QeA3GtgKocuLfGtQLqlPMYSN8VUGH9hZsEVkABwCXQw+Iph0M68mcpLF6Km0tAmcj6WwF10cThSsnRNbTnDbgC6oKJw6yAAjDOMCQE+AKHQ2qzXXpXYY9WQEUMXgOKFVAAxhCGhABfYDZLk2KdR8K1/d/jWgF1iUDTuwKqvdF51Hwy8J8ZMOmCeTUD7FfDCigAYwyBBfA2k8kZIEKipPirB76vdwXUpfarOXfWGWzqypzHQC5aAdXPfjWhMQx5AvAZlxVYtm3bpqefflpVVVWaP3++nn32WaWlpQ14f0NDgx599FHt3r1b9fX1Sk5OVm5urr70pS9Jkux2u/7lX/5FL7zwgqqqqpSQkKBvfOMb+t73vicT/xUIOA2pBtRAK6AuONdaI9k7pLPHncdABlsBFTbFOZfrcupZAYCHPA4sO3fuVHZ2trZv36709HTl5uZq5cqVOnz4sGJjYy+6v7OzU3/zN3+j2NhY/eY3v1FiYqJOnjypyMhI1z0/+tGP9NOf/lT/8z//o7lz5+q9997T+vXrFRERoQceeOAzfUFgQhnyCqiqS+9XM9QVUDINrQaUf9Cwf1UAE4vHk27T09O1ePFiPffcc5Ikh8OhpKQk3X///dq0adNF92/fvl1PP/20Dh06JH///v9L7Mtf/rLi4uKUl5fnOve1r31NwcHBeuGFF4bULibdAsPI3u3siRmwBpSnK6CiB6kBxQooYKIakUm3nZ2dKi4u1ubNm13nzGazMjMzVVhY2O9nXn31VWVkZOi+++7TK6+8opiYGH3961/XI488IovFOT6+ZMkSPf/88yotLdXs2bP1wQcfqKCgQM8888yAbeno6FBHR4fbFwYwTCx+54OFFvV/T78roPrprek+J7XVOY+qjwb+M/uugOpvv5qgCCYLAxOUR4HFZrPJbrcrLi7O7XxcXJwOHTrU72eOHTumN954Q7fddpv++Mc/6siRI/r2t7+trq4uPf7445KkTZs2qampSVdeeaUsFovsdru2bt2q2267bcC25OTk6IknnvCk+QCG02ddAXXh5OGOpqGtgPIPvcSS7p6JwyHRhBpgHBrxVUIOh0OxsbF6/vnnZbFYtHDhQlVUVOjpp592BZZf//rX+tWvfqUXX3xRc+fO1fvvv6+NGzcqISFBWVlZ/T538+bNys7Odv1zU1OTkpKSRvrrAPCEJyugmi+xq3DvCqiu1iGugBqgSjcroIAxy6PAYrVaZbFYVF1d7Xa+urpa8fHx/X5mypQp8vf3dw3/SNJVV12lqqoqdXZ2KiAgQA899JA2bdqktWvXSpLmzZunkydPKicnZ8DAEhgYqMBAtjIHxoXeFVAxVwx8j2sF1CX2q3GtgDrhPAbSuwLqUvvVsAIK8CkeBZaAgAAtXLhQ+fn5Wr16tSRnD0p+fr42bNjQ72eWLl2qF198UQ6HQ+aeqralpaWaMmWKAgICJEltbW2ua70sFoscDoen3wfAeDWkFVCdF4SaCvffu4aiKj/DCqg+PTdhCayAAkaJx0NC2dnZysrK0qJFi5SWlqbc3Fy1trZq/fr1kqR169YpMTFROTk5kqR//Md/1HPPPacHH3xQ999/v8rKyvTDH/7QbbnyTTfdpK1bt2ratGmaO3euDhw4oGeeeUbf/OY3h+lrApgQ/AKkycnOYyCXXAF1wbCUo8u5vLulSjpTMvDzBlsBFTZFCpw0/N8VmGA8Dixr1qxRbW2ttmzZoqqqKi1YsEB79uxxTcQtLy936y1JSkrSa6+9pu985zu65pprlJiYqAcffFCPPPKI655nn31Wjz32mL797W+rpqZGCQkJuueee7Rly5Zh+IoAcIEhr4Cqu8SSbg9XQAVGuFfm7m+/GlZAAZdE8UMAuBy9K6AuGnbqs8y7Y4hbLrhWQA00YZgVUBifKH4IACPpwhVQcXMHvu+iFVD9rIY6V3/5K6D67lczKZYVUBiXCCwAMJKGsgKq69zAG+/1Th5uqR7aCiiT5dI1oFgBhTGKwAIA3uYfPLQVUC2D1IBqrpQMu9R02nkMyOTsiRlwsjAroOB7CCwAMBb4BUiR05zHQFwroPqr2H1Bb42909lj01ItnTkw8PN6V0D1Vyah9/esgMIoIbAAwHjhtgJqYf/39LcCqu/E4caKYVgBdcERFMlkYXxmBBYAmEjMZmlSjPNIWND/PYYhtTf0M+x0YW9NpdTR6DxqG6XaTwf+M/1DLj2nhhVQGAICCwDAnckkBU92HpdaAdXR3P+qp4tWQLVJdUecx0AsAT2ThQcINKyAmvAILACAyxMYJsWESTGzB76ndwVUf2USen/fUuOcV9Nw0nkM5KIVUP0MRYVNYQXUOEVgAQCMnM+0AuqCYzhWQF0YdvyDh/2rYmQRWAAA3jWUFVAOu7MnZsA5NR6ugAqOGnhJd2+hy8Cw4f+uuGwEFgCA7zNbenb5naIBV0AZxtBqQHW1OefWnKuXqi+1Aip8CDWgIpksPEoILACA8cFkkkKtzmPK/P7vuWgFVH+Thc/0rIBqkmqbpNpDA/+ZfVdAuYad+qyAuqAoMC4PgQUAMHF81hVQF9aFaqsbhhVQPcekOFZADYLAAgBAX0NdAeVa/TQcK6DiL71fzaR453yfCYrAAgDA5fAPlqJmOI+B2Luk5qHWgKpwHgPqWQE1WG/NOF0BRWABAGCkWPylyCTnMZC+K6CaB9iM78IVUJXvD/y8i1ZA9TNxeAyugCKwAADgTZ95BdQFwWa4VkCFTXHO8/GhFVAEFgAAfN2QV0A1DlIDyoMVUH7BFweajPucbfACAgsAAOOBySQFRzqPuDkD39fR0mfYqZ/hp7Y6Z8Xu+qPOo1f6vSP9LQZEYAEAYCIJnCQFpkrW1IHv6WqXmvspkRAaM3rt7IPAAgAA3PkHDb4CapSx9R4AAPB5BBYAAODzCCwAAMDnEVgAAIDPI7AAAACfR2ABAAA+j8ACAAB8HoEFAAD4PAILAADweQQWAADg8wgsAADA5xFYAACAzyOwAAAAnzduqjUbhiFJampq8nJLAADAUPX+3O79OT6QcRNYmpubJUlJSUlebgkAAPBUc3OzIiIiBrxuMgaLNGOEw+HQmTNnFBYWJpPJNGzPbWpqUlJSkk6dOqXw8PBhey7c8Z5HD+96dPCeRwfveXSM5Hs2DEPNzc1KSEiQ2TzwTJVx08NiNps1derUEXt+eHg4/2cYBbzn0cO7Hh2859HBex4dI/WeL9Wz0otJtwAAwOcRWAAAgM8jsAwiMDBQjz/+uAIDA73dlHGN9zx6eNejg/c8OnjPo8MX3vO4mXQLAADGL3pYAACAzyOwAAAAn0dgAQAAPo/AAgAAfB6BZRDbtm1TSkqKgoKClJ6erqKiIm83aUzLycnR4sWLFRYWptjYWK1evVqHDx92u6e9vV333XefoqOjNWnSJH3ta19TdXW1l1o89j311FMymUzauHGj6xzvePhUVFTo9ttvV3R0tIKDgzVv3jy99957ruuGYWjLli2aMmWKgoODlZmZqbKyMi+2eOyx2+167LHHNH36dAUHB2vmzJn6/ve/71Z7hvfsub179+qmm25SQkKCTCaTfve737ldH8o7ra+v12233abw8HBFRkbqW9/6llpaWkamwQYGtGPHDiMgIMD42c9+Znz88cfGXXfdZURGRhrV1dXebtqYtXLlSuPnP/+5cfDgQeP99983vvSlLxnTpk0zWlpaXPfce++9RlJSkpGfn2+89957xvXXX28sWbLEi60eu4qKioyUlBTjmmuuMR588EHXed7x8KivrzeSk5ONb3zjG8b+/fuNY8eOGa+99ppx5MgR1z1PPfWUERERYfzud78zPvjgA+Pmm282pk+fbpw7d86LLR9btm7dakRHRxt/+MMfjOPHjxu7du0yJk2aZPz4xz923cN79twf//hH49FHHzV2795tSDJefvllt+tDeadf/OIXjfnz5xvvvPOOsW/fPmPWrFnGrbfeOiLtJbBcQlpamnHfffe5/tlutxsJCQlGTk6OF1s1vtTU1BiSjLfeesswDMNoaGgw/P39jV27drnu+fTTTw1JRmFhobeaOSY1Nzcbqampxp/+9CfjhhtucAUW3vHweeSRR4xly5YNeN3hcBjx8fHG008/7TrX0NBgBAYGGi+99NJoNHFcWLVqlfHNb37T7dxXv/pV47bbbjMMg/c8HPoGlqG8008++cSQZLz77ruue/7f//t/hslkMioqKoa9jQwJDaCzs1PFxcXKzMx0nTObzcrMzFRhYaEXWza+NDY2SpKioqIkScXFxerq6nJ771deeaWmTZvGe/fQfffdp1WrVrm9S4l3PJxeffVVLVq0SLfccotiY2N17bXX6r/+679c148fP66qqiq3dx0REaH09HTetQeWLFmi/Px8lZaWSpI++OADFRQU6G//9m8l8Z5HwlDeaWFhoSIjI7Vo0SLXPZmZmTKbzdq/f/+wt2ncFD8cbjabTXa7XXFxcW7n4+LidOjQIS+1anxxOBzauHGjli5dqquvvlqSVFVVpYCAAEVGRrrdGxcXp6qqKi+0cmzasWOHSkpK9O677150jXc8fI4dO6af/vSnys7O1ne/+129++67euCBBxQQEKCsrCzX++zv7xHe9dBt2rRJTU1NuvLKK2WxWGS327V161bddtttksR7HgFDeadVVVWKjY11u+7n56eoqKgRee8EFnjNfffdp4MHD6qgoMDbTRlXTp06pQcffFB/+tOfFBQU5O3mjGsOh0OLFi3SD3/4Q0nStddeq4MHD2r79u3KysrycuvGj1//+tf61a9+pRdffFFz587V+++/r40bNyohIYH3PIEwJDQAq9Uqi8Vy0cqJ6upqxcfHe6lV48eGDRv0hz/8QW+++aamTp3qOh8fH6/Ozk41NDS43c97H7ri4mLV1NTouuuuk5+fn/z8/PTWW2/pJz/5ifz8/BQXF8c7HiZTpkzRnDlz3M5dddVVKi8vlyTX++Tvkc/moYce0qZNm7R27VrNmzdPd9xxh77zne8oJydHEu95JAzlncbHx6umpsbtend3t+rr60fkvRNYBhAQEKCFCxcqPz/fdc7hcCg/P18ZGRlebNnYZhiGNmzYoJdffllvvPGGpk+f7nZ94cKF8vf3d3vvhw8fVnl5Oe99iD7/+c/ro48+0vvvv+86Fi1apNtuu831e97x8Fi6dOlFy/JLS0uVnJwsSZo+fbri4+Pd3nVTU5P279/Pu/ZAW1ubzGb3H1cWi0UOh0MS73kkDOWdZmRkqKGhQcXFxa573njjDTkcDqWnpw9/o4Z9Gu84smPHDiMwMND4xS9+YXzyySfG3XffbURGRhpVVVXebtqY9Y//+I9GRESE8Ze//MWorKx0HW1tba577r33XmPatGnGG2+8Ybz33ntGRkaGkZGR4cVWj30XrhIyDN7xcCkqKjL8/PyMrVu3GmVlZcavfvUrIyQkxHjhhRdc9zz11FNGZGSk8corrxgffvih8Xd/93cst/VQVlaWkZiY6FrWvHv3bsNqtRoPP/yw6x7es+eam5uNAwcOGAcOHDAkGc8884xx4MAB4+TJk4ZhDO2dfvGLXzSuvfZaY//+/UZBQYGRmprKsmZvefbZZ41p06YZAQEBRlpamvHOO+94u0ljmqR+j5///Oeue86dO2d8+9vfNiZPnmyEhIQYX/nKV4zKykrvNXoc6BtYeMfD5/e//71x9dVXG4GBgcaVV15pPP/8827XHQ6H8dhjjxlxcXFGYGCg8fnPf944fPiwl1o7NjU1NRkPPvigMW3aNCMoKMiYMWOG8eijjxodHR2ue3jPnnvzzTf7/fs4KyvLMIyhvdO6ujrj1ltvNSZNmmSEh4cb69evN5qbm0ekvSbDuGCrQAAAAB/EHBYAAODzCCwAAMDnEVgAAIDPI7AAAACfR2ABAAA+j8ACAAB8HoEFAAD4PAILAADweQQWAADg8wgsAADA5xFYAACAzyOwAAAAn/f/Ad2j3e4CAdOHAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(np.log10(alpha_list), train_score)\n",
        "plt.plot(np.log10(alpha_list), test_score)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "dfz8hqtlrnQm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "11719074-8457-48aa-c489-aaaec85d8fe6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score_df = pd.DataFrame({'alpha': alpha_list,\n",
        "                         'train_score': train_score,\n",
        "                         'test_score': test_score})\n",
        "score_df"
      ],
      "metadata": {
        "id": "bv_5RaewsR24",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "fc2772f7-eb53-438f-ad88-bb506a1490d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      alpha  train_score  test_score\n",
              "0    0.0001     0.803084    0.698386\n",
              "1    0.0010     0.798235    0.689357\n",
              "2    0.0100     0.795516    0.689294\n",
              "3    0.1000     0.787826    0.685612\n",
              "4    1.0000     0.776387    0.683355\n",
              "5   10.0000     0.769046    0.687365\n",
              "6  100.0000     0.724801    0.669810"
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>alpha</th>\n",
              "      <th>train_score</th>\n",
              "      <th>test_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0001</td>\n",
              "      <td>0.803084</td>\n",
              "      <td>0.698386</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0010</td>\n",
              "      <td>0.798235</td>\n",
              "      <td>0.689357</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0100</td>\n",
              "      <td>0.795516</td>\n",
              "      <td>0.689294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.1000</td>\n",
              "      <td>0.787826</td>\n",
              "      <td>0.685612</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.776387</td>\n",
              "      <td>0.683355</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>10.0000</td>\n",
              "      <td>0.769046</td>\n",
              "      <td>0.687365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>100.0000</td>\n",
              "      <td>0.724801</td>\n",
              "      <td>0.669810</td>\n",
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              "  </tbody>\n",
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              "</div>\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fbcd874f-499a-48b0-bcaa-e8ac2273e902')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-fbcd874f-499a-48b0-bcaa-e8ac2273e902 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-fbcd874f-499a-48b0-bcaa-e8ac2273e902');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-9b19c099-aee4-4013-abdf-606318410aee\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9b19c099-aee4-4013-abdf-606318410aee')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9b19c099-aee4-4013-abdf-606318410aee button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_3df7dd28-9f98-420d-8954-5864264d2597\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('score_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_3df7dd28-9f98-420d-8954-5864264d2597 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('score_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "score_df",
              "summary": "{\n  \"name\": \"score_df\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"alpha\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 37.27676225078337,\n        \"min\": 0.0001,\n        \"max\": 100.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.0001,\n          0.001,\n          10.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"train_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.026906149511648207,\n        \"min\": 0.7248009487886979,\n        \"max\": 0.8030843894475233,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.8030843894475233,\n          0.7982348495561664,\n          0.7690456420058942\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"test_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.008626273324149211,\n        \"min\": 0.6698097578963713,\n        \"max\": 0.6983861619807248,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.6983861619807248,\n          0.6893571389511757,\n          0.6873651945306054\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score_df['diff'] = score_df['train_score'] - score_df['test_score']"
      ],
      "metadata": {
        "id": "EwQ-cYnUsxDJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "score_df"
      ],
      "metadata": {
        "id": "zct3M8e-tHsm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "f5f98153-9639-4d3a-c94b-4f46d15e8c28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      alpha  train_score  test_score      diff\n",
              "0    0.0001     0.803084    0.698386  0.104698\n",
              "1    0.0010     0.798235    0.689357  0.108878\n",
              "2    0.0100     0.795516    0.689294  0.106221\n",
              "3    0.1000     0.787826    0.685612  0.102214\n",
              "4    1.0000     0.776387    0.683355  0.093032\n",
              "5   10.0000     0.769046    0.687365  0.081680\n",
              "6  100.0000     0.724801    0.669810  0.054991"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-376d52f2-6b89-4eb8-bf5b-0b6e04f6e17d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>alpha</th>\n",
              "      <th>train_score</th>\n",
              "      <th>test_score</th>\n",
              "      <th>diff</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0001</td>\n",
              "      <td>0.803084</td>\n",
              "      <td>0.698386</td>\n",
              "      <td>0.104698</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0010</td>\n",
              "      <td>0.798235</td>\n",
              "      <td>0.689357</td>\n",
              "      <td>0.108878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0100</td>\n",
              "      <td>0.795516</td>\n",
              "      <td>0.689294</td>\n",
              "      <td>0.106221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.1000</td>\n",
              "      <td>0.787826</td>\n",
              "      <td>0.685612</td>\n",
              "      <td>0.102214</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.776387</td>\n",
              "      <td>0.683355</td>\n",
              "      <td>0.093032</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>10.0000</td>\n",
              "      <td>0.769046</td>\n",
              "      <td>0.687365</td>\n",
              "      <td>0.081680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>100.0000</td>\n",
              "      <td>0.724801</td>\n",
              "      <td>0.669810</td>\n",
              "      <td>0.054991</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-376d52f2-6b89-4eb8-bf5b-0b6e04f6e17d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-376d52f2-6b89-4eb8-bf5b-0b6e04f6e17d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-376d52f2-6b89-4eb8-bf5b-0b6e04f6e17d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b63e61c8-9835-41de-9138-93db8c8019bf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_b12520d0-83c0-4c48-a890-1aa3c8ac93b7\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('score_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_b12520d0-83c0-4c48-a890-1aa3c8ac93b7 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('score_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "score_df",
              "summary": "{\n  \"name\": \"score_df\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"alpha\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 37.27676225078337,\n        \"min\": 0.0001,\n        \"max\": 100.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.0001,\n          0.001,\n          10.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"train_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.026906149511648207,\n        \"min\": 0.7248009487886979,\n        \"max\": 0.8030843894475233,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.8030843894475233,\n          0.7982348495561664,\n          0.7690456420058942\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"test_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.008626273324149211,\n        \"min\": 0.6698097578963713,\n        \"max\": 0.6983861619807248,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.6983861619807248,\n          0.6893571389511757,\n          0.6873651945306054\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"diff\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.01924135880783578,\n        \"min\": 0.054991190892326625,\n        \"max\": 0.10887771060499074,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.10469822746679847,\n          0.10887771060499074,\n          0.0816804474752888\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 규제조절 - RMSE\n",
        "train_score = []\n",
        "test_score = []\n",
        "alpha_list = [0.0001, 0.001, 0.01, 0.1, 1, 10, 100]\n",
        "for alpha in alpha_list:\n",
        "  ridge = Ridge(alpha = alpha)\n",
        "  ridge.fit(X_train_scale, y_train)\n",
        "  y_train_pred = ridge.predict(X_train_scale)\n",
        "  y_test_pred = ridge.predict(X_test_scale)\n",
        "  train_score.append(mean_squared_error(y_train, y_train_pred, squared = False))\n",
        "  test_score.append(mean_squared_error(y_test, y_test_pred, squared = False))"
      ],
      "metadata": {
        "id": "1QHp7Ss2uk_5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(np.log10(alpha_list), train_score)\n",
        "plt.plot(np.log10(alpha_list), test_score)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "ki0_m6jEulAD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "496588e4-07c3-4794-cae7-be8f5a92a537"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score_df = pd.DataFrame({'alpha': alpha_list,\n",
        "                         'train_score': train_score,\n",
        "                         'test_score': test_score})\n",
        "score_df"
      ],
      "metadata": {
        "id": "5IDODGJDulAD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "8a3e46f1-05b8-4c34-c13b-a0b010bb846b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      alpha  train_score  test_score\n",
              "0    0.0001     3.550700    3.994600\n",
              "1    0.0010     3.594157    4.053950\n",
              "2    0.0100     3.618296    4.054359\n",
              "3    0.1000     3.685698    4.078312\n",
              "4    1.0000     3.783747    4.092927\n",
              "5   10.0000     3.845360    4.066927\n",
              "6  100.0000     4.197565    4.179552"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a10fdb40-0858-4c67-9d02-908b06f889b1\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>alpha</th>\n",
              "      <th>train_score</th>\n",
              "      <th>test_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0001</td>\n",
              "      <td>3.550700</td>\n",
              "      <td>3.994600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0010</td>\n",
              "      <td>3.594157</td>\n",
              "      <td>4.053950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0100</td>\n",
              "      <td>3.618296</td>\n",
              "      <td>4.054359</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.1000</td>\n",
              "      <td>3.685698</td>\n",
              "      <td>4.078312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0000</td>\n",
              "      <td>3.783747</td>\n",
              "      <td>4.092927</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>10.0000</td>\n",
              "      <td>3.845360</td>\n",
              "      <td>4.066927</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>100.0000</td>\n",
              "      <td>4.197565</td>\n",
              "      <td>4.179552</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        document.querySelector('#df-a10fdb40-0858-4c67-9d02-908b06f889b1 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-a10fdb40-0858-4c67-9d02-908b06f889b1');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    display: none;\n",
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              "    padding: 0;\n",
              "    width: 32px;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-top-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d61f7492-411b-4012-9a70-aeb1d87aca04 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_1cf4cbd1-611a-44e7-b75c-9c99f9aa588b\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('score_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_1cf4cbd1-611a-44e7-b75c-9c99f9aa588b button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('score_df');\n",
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "score_df",
              "summary": "{\n  \"name\": \"score_df\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"alpha\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 37.27676225078337,\n        \"min\": 0.0001,\n        \"max\": 100.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.0001,\n          0.001,\n          10.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"train_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.22208931788977168,\n        \"min\": 3.5507004489726954,\n        \"max\": 4.197564981032865,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          3.5507004489726954,\n          3.594156963835008,\n          3.8453603389494653\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"test_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05576637853672723,\n        \"min\": 3.99459995028362,\n        \"max\": 4.179552434999132,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          3.99459995028362,\n          4.0539496407031015,\n          4.066926501656847\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score_df['diff'] = score_df['train_score'] - score_df['test_score']"
      ],
      "metadata": {
        "id": "xEJE-2qWulAD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "score_df"
      ],
      "metadata": {
        "id": "gxf0mOWyulAE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "0f531560-9231-41d1-d9d1-6adf68a086c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      alpha  train_score  test_score      diff\n",
              "0    0.0001     3.550700    3.994600 -0.443900\n",
              "1    0.0010     3.594157    4.053950 -0.459793\n",
              "2    0.0100     3.618296    4.054359 -0.436063\n",
              "3    0.1000     3.685698    4.078312 -0.392615\n",
              "4    1.0000     3.783747    4.092927 -0.309179\n",
              "5   10.0000     3.845360    4.066927 -0.221566\n",
              "6  100.0000     4.197565    4.179552  0.018013"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3e65a185-e101-44aa-954d-352b1b290764\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>alpha</th>\n",
              "      <th>train_score</th>\n",
              "      <th>test_score</th>\n",
              "      <th>diff</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0001</td>\n",
              "      <td>3.550700</td>\n",
              "      <td>3.994600</td>\n",
              "      <td>-0.443900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0010</td>\n",
              "      <td>3.594157</td>\n",
              "      <td>4.053950</td>\n",
              "      <td>-0.459793</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0100</td>\n",
              "      <td>3.618296</td>\n",
              "      <td>4.054359</td>\n",
              "      <td>-0.436063</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.1000</td>\n",
              "      <td>3.685698</td>\n",
              "      <td>4.078312</td>\n",
              "      <td>-0.392615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0000</td>\n",
              "      <td>3.783747</td>\n",
              "      <td>4.092927</td>\n",
              "      <td>-0.309179</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>10.0000</td>\n",
              "      <td>3.845360</td>\n",
              "      <td>4.066927</td>\n",
              "      <td>-0.221566</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>100.0000</td>\n",
              "      <td>4.197565</td>\n",
              "      <td>4.179552</td>\n",
              "      <td>0.018013</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "</div>\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
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              "\n",
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              "\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
              "    <script>\n",
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              "        document.querySelector('#df-3e65a185-e101-44aa-954d-352b1b290764 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3e65a185-e101-44aa-954d-352b1b290764');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-bd26ce29-5fb2-4a62-b623-2649cdf683ef\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bd26ce29-5fb2-4a62-b623-2649cdf683ef')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-bd26ce29-5fb2-4a62-b623-2649cdf683ef button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_269a52d0-bae0-475a-973d-8d60141357e5\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('score_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_269a52d0-bae0-475a-973d-8d60141357e5 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('score_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "score_df",
              "summary": "{\n  \"name\": \"score_df\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"alpha\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 37.27676225078337,\n        \"min\": 0.0001,\n        \"max\": 100.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.0001,\n          0.001,\n          10.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"train_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.22208931788977168,\n        \"min\": 3.5507004489726954,\n        \"max\": 4.197564981032865,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          3.5507004489726954,\n          3.594156963835008,\n          3.8453603389494653\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"test_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05576637853672723,\n        \"min\": 3.99459995028362,\n        \"max\": 4.179552434999132,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          3.99459995028362,\n          4.0539496407031015,\n          4.066926501656847\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"diff\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1720909181460222,\n        \"min\": -0.45979267686809333,\n        \"max\": 0.01801254603373259,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          -0.44389950131092437,\n          -0.45979267686809333,\n          -0.22156616270738194\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 최적의 알파로 최종 모델\n",
        "ridge = Ridge(alpha=0.0001)\n",
        "ridge.fit(X_train_scale, y_train)\n",
        "\n",
        "print(ridge.score(X_train_scale, y_train))\n",
        "print(ridge.score(X_test_scale, y_test))\n",
        "\n",
        "y_train_pred = ridge.predict(X_train_scale)\n",
        "y_test_pred = ridge.predict(X_test_scale)\n",
        "train_rmse = mean_squared_error(y_train, y_train_pred, squared = False)\n",
        "test_rmse = mean_squared_error(y_test, y_test_pred, squared = False)\n",
        "print(\"train RMSE: \", train_rmse)\n",
        "print(\"test RMSE: \", test_rmse)\n",
        "train_mae = mean_absolute_error(y_train, y_train_pred)\n",
        "test_mae = mean_absolute_error(y_test, y_test_pred)\n",
        "print(\"train MAE: \", train_mae)\n",
        "print(\"test MAE: \", test_mae)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bq2DEipu2wwf",
        "outputId": "79c30e3d-6b36-499d-d6e8-1b2b573b3f4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8030843894475233\n",
            "0.6983861619807248\n",
            "train RMSE:  3.5507004489726954\n",
            "test RMSE:  3.99459995028362\n",
            "train MAE:  2.5396359436875042\n",
            "test MAE:  2.9704193363532414\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 다항회귀분석 - 라쏘 L1"
      ],
      "metadata": {
        "id": "zCsWLj2Kwiv5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "id": "Oq7O92qjwiv8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a85ef8a4-100e-427f-9643-5d6ebe561825"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['mpg', 'cylinders', 'displacement', 'horsepower', 'weight',\n",
              "       'acceleration', 'model_year', 'origin', 'car_name'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 전체 데이터\n",
        "X = df.loc[:, ['cylinders', 'horsepower', 'weight']]\n",
        "y = df['mpg']\n",
        "\n",
        "print(\"X 데이터: \", X.shape)\n",
        "print(\"y 데이터: \", y.shape)"
      ],
      "metadata": {
        "id": "E6jiPh2Kwiv8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6a92a3cd-3548-43c9-a9f2-77f04d205a16"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X 데이터:  (392, 3)\n",
            "y 데이터:  (392,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터 분할 = 훈련 데이터 / 테스트 데이터 = 7:3\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
        "                                                                      test_size = 0.3,\n",
        "                                                                      random_state = 42)\n",
        "print(\"train 데이터: \", X_train.shape)\n",
        "print(\"test 데이터: \", X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "acab2704-1efa-4cb1-f817-c922acc0c56e",
        "id": "KSovw2suNAgd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train 데이터:  (274, 3)\n",
            "test 데이터:  (118, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "# 다항식\n",
        "poly = PolynomialFeatures(degree = 5, include_bias = False)\n",
        "X_train_poly = poly.fit_transform(X_train)\n",
        "X_test_poly = poly.transform(X_test)\n",
        "\n",
        "print(\"X_train 데이터: \", X_train.shape)\n",
        "print(\"X_train_poly 데이터: \", X_train_poly.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9e1ae915-51dc-4180-df48-6aec6af965ba",
        "id": "asfuWVfgNAgj"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train 데이터:  (274, 3)\n",
            "X_train_poly 데이터:  (274, 55)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 표준화\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "standard = StandardScaler()\n",
        "X_train_scale = standard.fit_transform(X_train_poly)\n",
        "X_test_scale = standard.transform(X_test_poly)"
      ],
      "metadata": {
        "id": "ps3iVFqvNAgj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 라쏘\n",
        "from sklearn.linear_model import Lasso\n",
        "lasso = Lasso()\n",
        "# 모델 훈련\n",
        "lasso.fit(X_train_scale, y_train)"
      ],
      "metadata": {
        "id": "uM-XltRDwiv9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "outputId": "7d3a3e2e-ff2e-46a5-b38d-3948b4cdd421"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Lasso()"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Lasso()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 모델 평가\n",
        "# 적합도 측도 = 결정계수 = 설명력\n",
        "print(lasso.score(X_train_scale, y_train))\n",
        "print(lasso.score(X_test_scale, y_test))"
      ],
      "metadata": {
        "id": "_4qc7JbVwiv9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8905da50-100c-41d6-cb01-076ad3ce1e52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7050729763665712\n",
            "0.6759693341417214\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 계수 = 모델 파라미터\n",
        "# 절편\n",
        "intercept = lasso.intercept_;intercept"
      ],
      "metadata": {
        "id": "qs3kjiStwiv9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f19f9ce5-9805-443a-bf18-9c5f44cc2107"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "23.637591240875913"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 기울기\n",
        "slope = [round(x, 4) for x in lasso.coef_];print(slope)"
      ],
      "metadata": {
        "id": "AxqJx9P4wiv9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "82988d16-98c3-4842-89e8-4f2f02cc3960"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-0.4145, -1.6234, -3.9303, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.sum(lasso.coef_ == 0)\n",
        "# 전체 55개 중에서 52개 사용하지 않음"
      ],
      "metadata": {
        "id": "Jcl0c7bVwiv-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cd3449c3-90d7-4899-f291-1e2256abc51d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "52"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_poly_columns = poly.get_feature_names_out()"
      ],
      "metadata": {
        "id": "6mPj_WDrwiv-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for name, coef in zip(X_poly_columns, slope):\n",
        "  print(\"{}: {}\".format(name, coef))"
      ],
      "metadata": {
        "id": "dMUZ6fpxwiv-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "73633eac-9705-4dbf-8dd0-43ae4303c66a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cylinders: -0.4145\n",
            "horsepower: -1.6234\n",
            "weight: -3.9303\n",
            "cylinders^2: -0.0\n",
            "cylinders horsepower: -0.0\n",
            "cylinders weight: -0.0\n",
            "horsepower^2: -0.0\n",
            "horsepower weight: -0.0\n",
            "weight^2: -0.0\n",
            "cylinders^3: -0.0\n",
            "cylinders^2 horsepower: -0.0\n",
            "cylinders^2 weight: -0.0\n",
            "cylinders horsepower^2: -0.0\n",
            "cylinders horsepower weight: -0.0\n",
            "cylinders weight^2: -0.0\n",
            "horsepower^3: -0.0\n",
            "horsepower^2 weight: -0.0\n",
            "horsepower weight^2: -0.0\n",
            "weight^3: -0.0\n",
            "cylinders^4: -0.0\n",
            "cylinders^3 horsepower: -0.0\n",
            "cylinders^3 weight: -0.0\n",
            "cylinders^2 horsepower^2: -0.0\n",
            "cylinders^2 horsepower weight: -0.0\n",
            "cylinders^2 weight^2: -0.0\n",
            "cylinders horsepower^3: -0.0\n",
            "cylinders horsepower^2 weight: -0.0\n",
            "cylinders horsepower weight^2: -0.0\n",
            "cylinders weight^3: -0.0\n",
            "horsepower^4: -0.0\n",
            "horsepower^3 weight: -0.0\n",
            "horsepower^2 weight^2: -0.0\n",
            "horsepower weight^3: -0.0\n",
            "weight^4: -0.0\n",
            "cylinders^5: -0.0\n",
            "cylinders^4 horsepower: -0.0\n",
            "cylinders^4 weight: -0.0\n",
            "cylinders^3 horsepower^2: -0.0\n",
            "cylinders^3 horsepower weight: -0.0\n",
            "cylinders^3 weight^2: -0.0\n",
            "cylinders^2 horsepower^3: -0.0\n",
            "cylinders^2 horsepower^2 weight: -0.0\n",
            "cylinders^2 horsepower weight^2: -0.0\n",
            "cylinders^2 weight^3: -0.0\n",
            "cylinders horsepower^4: -0.0\n",
            "cylinders horsepower^3 weight: -0.0\n",
            "cylinders horsepower^2 weight^2: -0.0\n",
            "cylinders horsepower weight^3: -0.0\n",
            "cylinders weight^4: -0.0\n",
            "horsepower^5: -0.0\n",
            "horsepower^4 weight: -0.0\n",
            "horsepower^3 weight^2: -0.0\n",
            "horsepower^2 weight^3: -0.0\n",
            "horsepower weight^4: -0.0\n",
            "weight^5: -0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 표본회귀식\n",
        "equation = \"y_hat = \" + str(intercept)\n",
        "for coef, name in zip(slope, X_poly_columns):\n",
        "  equation += \" + {} * {}\".format(coef, name)\n",
        "print(equation)"
      ],
      "metadata": {
        "id": "L26eD8Eowiv-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2ef1bd19-ea19-4fd4-977f-abf7d9c55bb5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "y_hat = 23.637591240875913 + -0.4145 * cylinders + -1.6234 * horsepower + -3.9303 * weight + -0.0 * cylinders^2 + -0.0 * cylinders horsepower + -0.0 * cylinders weight + -0.0 * horsepower^2 + -0.0 * horsepower weight + -0.0 * weight^2 + -0.0 * cylinders^3 + -0.0 * cylinders^2 horsepower + -0.0 * cylinders^2 weight + -0.0 * cylinders horsepower^2 + -0.0 * cylinders horsepower weight + -0.0 * cylinders weight^2 + -0.0 * horsepower^3 + -0.0 * horsepower^2 weight + -0.0 * horsepower weight^2 + -0.0 * weight^3 + -0.0 * cylinders^4 + -0.0 * cylinders^3 horsepower + -0.0 * cylinders^3 weight + -0.0 * cylinders^2 horsepower^2 + -0.0 * cylinders^2 horsepower weight + -0.0 * cylinders^2 weight^2 + -0.0 * cylinders horsepower^3 + -0.0 * cylinders horsepower^2 weight + -0.0 * cylinders horsepower weight^2 + -0.0 * cylinders weight^3 + -0.0 * horsepower^4 + -0.0 * horsepower^3 weight + -0.0 * horsepower^2 weight^2 + -0.0 * horsepower weight^3 + -0.0 * weight^4 + -0.0 * cylinders^5 + -0.0 * cylinders^4 horsepower + -0.0 * cylinders^4 weight + -0.0 * cylinders^3 horsepower^2 + -0.0 * cylinders^3 horsepower weight + -0.0 * cylinders^3 weight^2 + -0.0 * cylinders^2 horsepower^3 + -0.0 * cylinders^2 horsepower^2 weight + -0.0 * cylinders^2 horsepower weight^2 + -0.0 * cylinders^2 weight^3 + -0.0 * cylinders horsepower^4 + -0.0 * cylinders horsepower^3 weight + -0.0 * cylinders horsepower^2 weight^2 + -0.0 * cylinders horsepower weight^3 + -0.0 * cylinders weight^4 + -0.0 * horsepower^5 + -0.0 * horsepower^4 weight + -0.0 * horsepower^3 weight^2 + -0.0 * horsepower^2 weight^3 + -0.0 * horsepower weight^4 + -0.0 * weight^5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train_pred = lasso.predict(X_train_scale)\n",
        "y_test_pred = lasso.predict(X_test_scale)"
      ],
      "metadata": {
        "id": "Qx6g50gMwiv-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 예측 성능 평가\n",
        "# RMSE, MAE\n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
        "train_rmse = mean_squared_error(y_train, y_train_pred, squared = False)\n",
        "test_rmse = mean_squared_error(y_test, y_test_pred, squared = False)\n",
        "print(\"train RMSE: \", train_rmse)\n",
        "print(\"test RMSE: \", test_rmse)\n",
        "train_mae = mean_absolute_error(y_train, y_train_pred)\n",
        "test_mae = mean_absolute_error(y_test, y_test_pred)\n",
        "print(\"train MAE: \", train_mae)\n",
        "print(\"test MAE: \", test_mae)"
      ],
      "metadata": {
        "id": "ph3thY3vwiv-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6397a33d-63dd-4b5a-f4a0-f9e23176e25c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train RMSE:  4.345414867036591\n",
            "test RMSE:  4.140384912549389\n",
            "train MAE:  3.282194698008912\n",
            "test MAE:  3.3131776458991924\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 실제값과 예측값 비교\n",
        "plt.figure(figsize = (5, 3))\n",
        "ax1 = sns.kdeplot(y, label = \"y\")\n",
        "ax2 = sns.kdeplot(y_train_pred, label = \"train y_hat\", ax = ax1)\n",
        "ax3 = sns.kdeplot(y_test_pred, label = \"test y_hat\", ax = ax1)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "6yuL_C_cwiv-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "outputId": "32710082-bda8-432e-f04b-02c7020d2ffd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 규제조절 - 결정계수\n",
        "train_score = []\n",
        "test_score = []\n",
        "alpha_list = [0.0001, 0.001, 0.01, 0.1, 1, 10, 100]\n",
        "for alpha in alpha_list:\n",
        "  lasso = Lasso(alpha = alpha)\n",
        "  lasso.fit(X_train_scale, y_train)\n",
        "  train_score.append(lasso.score(X_train_scale, y_train))\n",
        "  test_score.append(lasso.score(X_test_scale, y_test))"
      ],
      "metadata": {
        "id": "3LSgrBhswiv-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b2e449ed-0831-4dce-ef45-d1459123ee4a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.835e+03, tolerance: 1.754e+00\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.373e+03, tolerance: 1.754e+00\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.552e+01, tolerance: 1.754e+00\n",
            "  model = cd_fast.enet_coordinate_descent(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(np.log10(alpha_list), train_score)\n",
        "plt.plot(np.log10(alpha_list), test_score)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Q2D64VxRwiv_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "9e324059-b69e-4417-a279-b85f249aeb2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score_df = pd.DataFrame({'alpha': alpha_list,\n",
        "                         'train_score': train_score,\n",
        "                         'test_score': test_score})\n",
        "score_df"
      ],
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          "height": 269
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      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "      alpha  train_score  test_score\n",
              "0    0.0001     0.784311    0.682303\n",
              "1    0.0010     0.783782    0.682673\n",
              "2    0.0100     0.775729    0.681570\n",
              "3    0.1000     0.763339    0.688366\n",
              "4    1.0000     0.705073    0.675969\n",
              "5   10.0000     0.000000   -0.007664\n",
              "6  100.0000     0.000000   -0.007664"
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              "      <th></th>\n",
              "      <th>alpha</th>\n",
              "      <th>train_score</th>\n",
              "      <th>test_score</th>\n",
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              "      <th>0</th>\n",
              "      <td>0.0001</td>\n",
              "      <td>0.784311</td>\n",
              "      <td>0.682303</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0010</td>\n",
              "      <td>0.783782</td>\n",
              "      <td>0.682673</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0100</td>\n",
              "      <td>0.775729</td>\n",
              "      <td>0.681570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.1000</td>\n",
              "      <td>0.763339</td>\n",
              "      <td>0.688366</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.705073</td>\n",
              "      <td>0.675969</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>10.0000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.007664</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
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              "\n",
              "  @keyframes spin {\n",
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              "  }\n",
              "</style>\n",
              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('score_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "score_df",
              "summary": "{\n  \"name\": \"score_df\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"alpha\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 37.27676225078337,\n        \"min\": 0.0001,\n        \"max\": 100.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.0001,\n          0.001,\n          10.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"train_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.37302070820530453,\n        \"min\": 0.0,\n        \"max\": 0.7843107248012195,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.7843107248012195,\n          0.7837819978500905,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"test_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3366265865754062,\n        \"min\": -0.007663622529608727,\n        \"max\": 0.6883662187408771,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.6823031335932854,\n          0.6826728458109975,\n          -0.007663622529608727\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score_df['diff'] = score_df['train_score'] - score_df['test_score']"
      ],
      "metadata": {
        "id": "tW7XFqC8wiv_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "score_df"
      ],
      "metadata": {
        "id": "9N6pmk_cwiv_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "8e4b10af-d35a-4f6d-e41f-16ddfca62a82"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      alpha  train_score  test_score      diff\n",
              "0    0.0001     0.784311    0.682303  0.102008\n",
              "1    0.0010     0.783782    0.682673  0.101109\n",
              "2    0.0100     0.775729    0.681570  0.094160\n",
              "3    0.1000     0.763339    0.688366  0.074973\n",
              "4    1.0000     0.705073    0.675969  0.029104\n",
              "5   10.0000     0.000000   -0.007664  0.007664\n",
              "6  100.0000     0.000000   -0.007664  0.007664"
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>alpha</th>\n",
              "      <th>train_score</th>\n",
              "      <th>test_score</th>\n",
              "      <th>diff</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0001</td>\n",
              "      <td>0.784311</td>\n",
              "      <td>0.682303</td>\n",
              "      <td>0.102008</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0010</td>\n",
              "      <td>0.783782</td>\n",
              "      <td>0.682673</td>\n",
              "      <td>0.101109</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0100</td>\n",
              "      <td>0.775729</td>\n",
              "      <td>0.681570</td>\n",
              "      <td>0.094160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.1000</td>\n",
              "      <td>0.763339</td>\n",
              "      <td>0.688366</td>\n",
              "      <td>0.074973</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.705073</td>\n",
              "      <td>0.675969</td>\n",
              "      <td>0.029104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>10.0000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.007664</td>\n",
              "      <td>0.007664</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>100.0000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.007664</td>\n",
              "      <td>0.007664</td>\n",
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              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0e5b4cd1-7cfb-4060-a44d-f5dfddb35521')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  <style>\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
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              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-0e5b4cd1-7cfb-4060-a44d-f5dfddb35521 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-0e5b4cd1-7cfb-4060-a44d-f5dfddb35521');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
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              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-eb19ea6a-a024-429c-96d6-9a408c4b2c38 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_33731b32-4026-4a5b-87e5-b514978d3dc8\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('score_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_33731b32-4026-4a5b-87e5-b514978d3dc8 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('score_df');\n",
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              "  </div>\n",
              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "score_df",
              "summary": "{\n  \"name\": \"score_df\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"alpha\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 37.27676225078337,\n        \"min\": 0.0001,\n        \"max\": 100.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.0001,\n          0.001,\n          10.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"train_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.37302070820530453,\n        \"min\": 0.0,\n        \"max\": 0.7843107248012195,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.7843107248012195,\n          0.7837819978500905,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"test_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3366265865754062,\n        \"min\": -0.007663622529608727,\n        \"max\": 0.6883662187408771,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.6823031335932854,\n          0.6826728458109975,\n          -0.007663622529608727\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"diff\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.043352935568683644,\n        \"min\": 0.007663622529608727,\n        \"max\": 0.10200759120793412,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.10200759120793412,\n          0.10110915203909299,\n          0.007663622529608727\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 규제조절 - RMSE\n",
        "train_score = []\n",
        "test_score = []\n",
        "alpha_list = [0.0001, 0.001, 0.01, 0.1, 1, 10, 100]\n",
        "for alpha in alpha_list:\n",
        "  lasso = Lasso(alpha = alpha)\n",
        "  lasso.fit(X_train_scale, y_train)\n",
        "  y_train_pred = lasso.predict(X_train_scale)\n",
        "  y_test_pred = lasso.predict(X_test_scale)\n",
        "  train_score.append(mean_squared_error(y_train, y_train_pred, squared = False))\n",
        "  test_score.append(mean_squared_error(y_test, y_test_pred, squared = False))"
      ],
      "metadata": {
        "id": "KLUCKQmrwiv_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "61cab9a0-d116-45a7-d45a-18a97eda7df9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.835e+03, tolerance: 1.754e+00\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.373e+03, tolerance: 1.754e+00\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.552e+01, tolerance: 1.754e+00\n",
            "  model = cd_fast.enet_coordinate_descent(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(np.log10(alpha_list), train_score)\n",
        "plt.plot(np.log10(alpha_list), test_score)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "QddU7WuDwiv_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "efdbefbc-3e69-49ce-e8a1-a806fdea601c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score_df = pd.DataFrame({'alpha': alpha_list,\n",
        "                         'train_score': train_score,\n",
        "                         'test_score': test_score})\n",
        "score_df"
      ],
      "metadata": {
        "id": "uL3TVvGnwiv_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "4beade30-e4ed-4ff7-d6e0-99c38324863c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      alpha  train_score  test_score\n",
              "0    0.0001     3.716107    4.099719\n",
              "1    0.0010     3.720659    4.097333\n",
              "2    0.0100     3.789309    4.104448\n",
              "3    0.1000     3.892579    4.060410\n",
              "4    1.0000     4.345415    4.140385\n",
              "5   10.0000     8.001547    7.301388\n",
              "6  100.0000     8.001547    7.301388"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>alpha</th>\n",
              "      <th>train_score</th>\n",
              "      <th>test_score</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>0.0001</td>\n",
              "      <td>3.716107</td>\n",
              "      <td>4.099719</td>\n",
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              "      <th>1</th>\n",
              "      <td>0.0010</td>\n",
              "      <td>3.720659</td>\n",
              "      <td>4.097333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0100</td>\n",
              "      <td>3.789309</td>\n",
              "      <td>4.104448</td>\n",
              "    </tr>\n",
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              "      <th>3</th>\n",
              "      <td>0.1000</td>\n",
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              "      <td>4.140385</td>\n",
              "    </tr>\n",
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              "      <th>5</th>\n",
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              "      <td>8.001547</td>\n",
              "      <td>7.301388</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
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              "      <td>8.001547</td>\n",
              "      <td>7.301388</td>\n",
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              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-20e16f06-9943-4286-9f3f-698005c901e3 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-20e16f06-9943-4286-9f3f-698005c901e3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-3d349ea7-01fe-4a5c-b3c1-788996d24459\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3d349ea7-01fe-4a5c-b3c1-788996d24459')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3d349ea7-01fe-4a5c-b3c1-788996d24459 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_6af42159-3527-437f-ac62-bbd85fbedc03\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('score_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_6af42159-3527-437f-ac62-bbd85fbedc03 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('score_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "score_df",
              "summary": "{\n  \"name\": \"score_df\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"alpha\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 37.27676225078337,\n        \"min\": 0.0001,\n        \"max\": 100.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.0001,\n          0.001,\n          10.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"train_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.0163134959880824,\n        \"min\": 3.7161072311856995,\n        \"max\": 8.00154702428356,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          3.7161072311856995,\n          3.7206591576040955,\n          8.00154702428356\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"test_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.5620655831622443,\n        \"min\": 4.060410343107998,\n        \"max\": 7.3013883909703186,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4.0997193369980565,\n          4.09733316685455,\n          7.3013883909703186\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score_df['diff'] = score_df['train_score'] - score_df['test_score']"
      ],
      "metadata": {
        "id": "_63MWr-gwiv_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "score_df"
      ],
      "metadata": {
        "id": "BdARrWHywiv_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "f1a7e75d-d541-4f9d-e832-121556101f4d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      alpha  train_score  test_score      diff\n",
              "0    0.0001     3.716107    4.099719 -0.383612\n",
              "1    0.0010     3.720659    4.097333 -0.376674\n",
              "2    0.0100     3.789309    4.104448 -0.315139\n",
              "3    0.1000     3.892579    4.060410 -0.167831\n",
              "4    1.0000     4.345415    4.140385  0.205030\n",
              "5   10.0000     8.001547    7.301388  0.700159\n",
              "6  100.0000     8.001547    7.301388  0.700159"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-12826d5f-66ee-4dbe-83fd-0feba1e4b6c7\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>alpha</th>\n",
              "      <th>train_score</th>\n",
              "      <th>test_score</th>\n",
              "      <th>diff</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0001</td>\n",
              "      <td>3.716107</td>\n",
              "      <td>4.099719</td>\n",
              "      <td>-0.383612</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0010</td>\n",
              "      <td>3.720659</td>\n",
              "      <td>4.097333</td>\n",
              "      <td>-0.376674</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0100</td>\n",
              "      <td>3.789309</td>\n",
              "      <td>4.104448</td>\n",
              "      <td>-0.315139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.1000</td>\n",
              "      <td>3.892579</td>\n",
              "      <td>4.060410</td>\n",
              "      <td>-0.167831</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0000</td>\n",
              "      <td>4.345415</td>\n",
              "      <td>4.140385</td>\n",
              "      <td>0.205030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>10.0000</td>\n",
              "      <td>8.001547</td>\n",
              "      <td>7.301388</td>\n",
              "      <td>0.700159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>100.0000</td>\n",
              "      <td>8.001547</td>\n",
              "      <td>7.301388</td>\n",
              "      <td>0.700159</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-12826d5f-66ee-4dbe-83fd-0feba1e4b6c7')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-12826d5f-66ee-4dbe-83fd-0feba1e4b6c7 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-12826d5f-66ee-4dbe-83fd-0feba1e4b6c7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "  </div>\n",
              "\n",
              "\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d387b7cd-eeca-46a8-8c81-32ffaf2ed658 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_0c576c73-e94e-4a09-af51-77dbdb6805ea\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('score_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_0c576c73-e94e-4a09-af51-77dbdb6805ea button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('score_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "score_df",
              "summary": "{\n  \"name\": \"score_df\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"alpha\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 37.27676225078337,\n        \"min\": 0.0001,\n        \"max\": 100.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.0001,\n          0.001,\n          10.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"train_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.0163134959880824,\n        \"min\": 3.7161072311856995,\n        \"max\": 8.00154702428356,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          3.7161072311856995,\n          3.7206591576040955,\n          8.00154702428356\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"test_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.5620655831622443,\n        \"min\": 4.060410343107998,\n        \"max\": 7.3013883909703186,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4.0997193369980565,\n          4.09733316685455,\n          7.3013883909703186\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"diff\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.48653069771928764,\n        \"min\": -0.38361210581235694,\n        \"max\": 0.7001586333132419,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          -0.38361210581235694,\n          -0.37667400925045413,\n          0.7001586333132419\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 최적의 알파로 최종 모델\n",
        "lasso = Lasso(alpha=0.001)\n",
        "lasso.fit(X_train_scale, y_train)\n",
        "\n",
        "print(lasso.score(X_train_scale, y_train))\n",
        "print(lasso.score(X_test_scale, y_test))\n",
        "\n",
        "y_train_pred = lasso.predict(X_train_scale)\n",
        "y_test_pred = lasso.predict(X_test_scale)\n",
        "train_rmse = mean_squared_error(y_train, y_train_pred, squared = False)\n",
        "test_rmse = mean_squared_error(y_test, y_test_pred, squared = False)\n",
        "print(\"train RMSE: \", train_rmse)\n",
        "print(\"test RMSE: \", test_rmse)\n",
        "train_mae = mean_absolute_error(y_train, y_train_pred)\n",
        "test_mae = mean_absolute_error(y_test, y_test_pred)\n",
        "print(\"train MAE: \", train_mae)\n",
        "print(\"test MAE: \", test_mae)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xyC9R4VPOQ7n",
        "outputId": "2cff330e-2e88-4dd0-9377-b15f329f471d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7837819978500905\n",
            "0.6826728458109975\n",
            "train RMSE:  3.7206591576040955\n",
            "test RMSE:  4.09733316685455\n",
            "train MAE:  2.6996128919067024\n",
            "test MAE:  3.0372597376527417\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.373e+03, tolerance: 1.754e+00\n",
            "  model = cd_fast.enet_coordinate_descent(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 분류 분석"
      ],
      "metadata": {
        "id": "yv1hGYOZAP21"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8rdDSaZ5uji2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "a446316d-0039-4590-de3d-8981b7c96ca0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "z = np.arange(-10, 10, 0.5) # x축의 z값을 0.1의 간격으로 표시\n",
        "p_hat = 1 / (1 + np.exp(-z)) # np.exp 로 지수 함수 계산\n",
        "\n",
        "plt.plot(z, p_hat)\n",
        "plt.xlabel('z')\n",
        "plt.ylabel('p_hat')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "z = np.arange(-10, 10, 0.5) # x축의 z값을 0.1의 간격으로 표시\n",
        "p_hat = np.exp(z) / (1 + np.exp(z)) # np.exp 로 지수 함수 계산\n",
        "\n",
        "plt.plot(z, p_hat)\n",
        "plt.xlabel('z')\n",
        "plt.ylabel('p_hat')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "4K7YehgRAsed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "3df33abf-3fa7-478a-ed9f-dd1b29be30f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 퀴즈"
      ],
      "metadata": {
        "id": "SyIAolK0Usel"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "TP = 64; FN = 18; FP = 104; TN = 128\n",
        "# 정확도 = 정분류율\n",
        "acc = (TP + TN) / (TP + FN + FP + TN)\n",
        "print(\"정확도 = 정분류율\", acc)\n",
        "# 오분류율\n",
        "print(\"오분류율\", 1 - acc)\n",
        "# 정밀도\n",
        "# 예측한 것들 중에서 얼마나 잘 맞췄는가 = 모델 정확도\n",
        "prec = TP / (TP + FP)\n",
        "print(\"정밀도\", prec)\n",
        "# 재현율 = 민감도 = 소수집단 정분류율\n",
        "# 실제 참인 것들 중에서 얼마나 잘 예측(재현)하였는가\n",
        "reca = TP / (TP + FN)\n",
        "print(\"재현율\", reca)\n",
        "# 소수집단 오분류율\n",
        "print(\"소수집단 오분류율\", 1 - reca)\n",
        "# 특이도\n",
        "spec = TN / (FP + TN)\n",
        "print(\"특이도\", spec)\n",
        "# f1 점수\n",
        "f1s = 2 * prec * reca / (prec + reca)\n",
        "print(\"f1 점수\", f1s)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TgyHl6N4DQA6",
        "outputId": "297acda4-4cd2-4c81-ab11-d0b97d0abda5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "정확도 = 정분류율 0.6114649681528662\n",
            "오분류율 0.3885350318471338\n",
            "정밀도 0.38095238095238093\n",
            "재현율 0.7804878048780488\n",
            "소수집단 오분류율 0.2195121951219512\n",
            "특이도 0.5517241379310345\n",
            "f1 점수 0.512\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 로지스틱 회귀 분석 - 전체데이터"
      ],
      "metadata": {
        "id": "iOyo1utqAR_-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_excel('/content/drive/MyDrive/lsda02/data/mortgage.xlsx', engine = 'openpyxl')\n",
        "df.head()\n",
        "# 담보대출승인여부, 착수금비율, 소득대출비율"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "57afc8a3-6716-4630-ef23-71e5a534d1a1",
        "id": "Wu6J8jVd1ya9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   y     x1     x2\n",
              "0  1  16.35  49.94\n",
              "1  1  34.43  56.16\n",
              "2  1  39.19  36.89\n",
              "3  1  23.58  56.88\n",
              "4  0  29.92  27.05"
            ],
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c1bf62fb-4980-4ef2-9f36-78b3e80cc65f button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c1bf62fb-4980-4ef2-9f36-78b3e80cc65f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-8302cd35-642d-4bb0-b062-d63057cab1db\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8302cd35-642d-4bb0-b062-d63057cab1db')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8302cd35-642d-4bb0-b062-d63057cab1db button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 30,\n  \"fields\": [\n    {\n      \"column\": \"y\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"x1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.84284710820006,\n        \"min\": 10.12,\n        \"max\": 39.19,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          27.53,\n          37.91\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"x2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.94221591796663,\n        \"min\": 20.79,\n        \"max\": 56.88,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          25.63,\n          22.92\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b95b6b26-0927-4d6f-b476-ced250664bb5",
        "id": "fjbMOkj02kyV"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 30 entries, 0 to 29\n",
            "Data columns (total 3 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   y       30 non-null     int64  \n",
            " 1   x1      30 non-null     float64\n",
            " 2   x2      30 non-null     float64\n",
            "dtypes: float64(2), int64(1)\n",
            "memory usage: 848.0 bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['y'].unique()"
      ],
      "metadata": {
        "id": "XgfVG3ndBCzN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5c8ce9ec-3dc4-4ddd-aefd-411375d76297"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['y'].value_counts()"
      ],
      "metadata": {
        "id": "zFD7J3lABL_2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "30abcf77-f276-458c-f914-e0e567f4095d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "y\n",
              "1    17\n",
              "0    13\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "id": "RL2CG8bmBS5O",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "79fe8c63-60f0-417e-d7c3-e6bc83e14947"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['y', 'x1', 'x2'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 전체 데이터\n",
        "X = df.loc[:,['x1', 'x2']]\n",
        "y = df['y']\n",
        "\n",
        "print(\"X 데이터: \", X.shape)\n",
        "print(\"y 데이터: \", y.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6e8889b0-0f5a-4d3f-d3c4-3775cf2b0d64",
        "id": "WEfowuqm3MYW"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X 데이터:  (30, 2)\n",
            "y 데이터:  (30,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 로지스틱회귀모델\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "m1 = LogisticRegression();m1.fit(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "outputId": "555db4d7-2adb-4698-88a6-ec353b810a87",
        "id": "3gX9ECOo3X3M"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 계수 = 모델 파라미터\n",
        "# 절편\n",
        "intercept1 = round(m1.intercept_[0], 5);intercept1"
      ],
      "metadata": {
        "id": "v6O9O3pHCBg1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "13a29ec6-b773-4e02-8920-09877f9b2fe6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-9.31469"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 기울기\n",
        "slope1 = [round(x, 5) for x in m1.coef_[0]];print(slope1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d398a9e9-2aa2-4bd1-cde6-33a5c9cae004",
        "id": "33KYtH6S4IKw"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.13407, 0.17728]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for name, coef in zip(X.columns, slope1):\n",
        "  print(\"{}: {}\".format(name, coef))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d4ed8456-13e1-435c-ccae-c6f63b976e92",
        "id": "2MK62szy4IKx"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x1: 0.13407\n",
            "x2: 0.17728\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 표본회귀식\n",
        "equation1 = \"y_hat = \" + str(intercept1)\n",
        "for coef, name in zip(slope1, X.columns):\n",
        "  equation1 += \" + {} * {}\".format(coef, name)\n",
        "print(equation1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f6dca71d-cfe9-41b6-a7dc-0395914f3912",
        "id": "0TgjOsCs4IKx"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "y_hat = -9.31469 + 0.13407 * x1 + 0.17728 * x2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 예측\n",
        "y_pred1 = m1.predict(X)\n",
        "y_pred1[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d4021520-f493-4e62-a974-6f1640cca46c",
        "id": "71UIZOyC4hd-"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred1_proba = m1.predict_proba(X)\n",
        "y_pred1_proba[:5]"
      ],
      "metadata": {
        "id": "r7TRZnCPXMD1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1c019e2e-65fb-48f1-c890-0083f8d08ff8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.1505, 0.8495],\n",
              "       [0.0052, 0.9948],\n",
              "       [0.0773, 0.9227],\n",
              "       [0.0193, 0.9807],\n",
              "       [0.6243, 0.3757]])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "m1.classes_"
      ],
      "metadata": {
        "id": "_HwWkehAX0nL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3c19a282-9ab3-425d-c257-e0d7543bbae0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "zv = m1.decision_function(X)"
      ],
      "metadata": {
        "id": "VCOeYJAJX8Wf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "zv[:5]"
      ],
      "metadata": {
        "id": "Hohz-Ymlvpyk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d09dd62a-ac10-4f5c-9f4d-f9a48684df6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1.7306,  5.2573,  2.4794,  3.9302, -0.5079])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X.head()"
      ],
      "metadata": {
        "id": "v9oFHXIsPPgp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "e572ddbd-c8da-4ae8-ee1c-104e4518ff35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      x1     x2\n",
              "0  16.35  49.94\n",
              "1  34.43  56.16\n",
              "2  39.19  36.89\n",
              "3  23.58  56.88\n",
              "4  29.92  27.05"
            ],
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X",
              "summary": "{\n  \"name\": \"X\",\n  \"rows\": 30,\n  \"fields\": [\n    {\n      \"column\": \"x1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.84284710820006,\n        \"min\": 10.12,\n        \"max\": 39.19,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          27.53,\n          37.91,\n          11.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"x2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.94221591796663,\n        \"min\": 20.79,\n        \"max\": 56.88,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          25.63,\n          22.92,\n          32.96\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x1 = X['x1'][0];x2 = X['x2'][0]\n",
        "-9.31469 + 0.13407 * x1 + 0.17728 * x2"
      ],
      "metadata": {
        "id": "xUCKiuYJvwaW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a4d29614-86be-4239-d7d8-bcee1f0620c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.7307176999999978"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_pred1_proba[:5])\n",
        "from scipy.special import expit\n",
        "expit(zv)"
      ],
      "metadata": {
        "id": "ZG0yRnoqYMKD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "db1ae307-8717-4102-cde0-5983ad05b4ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.1505 0.8495]\n",
            " [0.0052 0.9948]\n",
            " [0.0773 0.9227]\n",
            " [0.0193 0.9807]\n",
            " [0.6243 0.3757]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.8495, 0.9948, 0.9227, 0.9807, 0.3757, 0.8743, 0.9861, 0.8911,\n",
              "       0.6339, 0.7016, 0.5346, 0.0837, 0.0631, 0.2251, 0.9123, 0.4579,\n",
              "       0.9671, 0.8868, 0.9868, 0.227 , 0.029 , 0.066 , 0.9842, 0.1305,\n",
              "       0.6644, 0.1657, 0.1506, 0.2535, 0.898 , 0.1034])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 성능 평가 - 정확도\n",
        "print(m1.score(X, y))\n",
        "print(np.mean(y == y_pred1))"
      ],
      "metadata": {
        "id": "t1cFtUpkPbos",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "011ae446-fc1f-4791-ac66-1ef29aeb6fb4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8666666666666667\n",
            "0.8666666666666667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# confusion matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "logit_cm = confusion_matrix(y, y_pred1)\n",
        "print(logit_cm)"
      ],
      "metadata": {
        "id": "QyXXTQgXeVeY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "294151a5-cc05-41e3-e6ce-96124e6f1de2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[11  2]\n",
            " [ 2 15]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "logit_rpt = classification_report(y, y_pred1)\n",
        "print(logit_rpt)"
      ],
      "metadata": {
        "id": "u3VG153SfAkl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6feb72c2-386d-4c2f-92d2-b37b73dd60a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.85      0.85      0.85        13\n",
            "           1       0.88      0.88      0.88        17\n",
            "\n",
            "    accuracy                           0.87        30\n",
            "   macro avg       0.86      0.86      0.86        30\n",
            "weighted avg       0.87      0.87      0.87        30\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(0.85 + 0.88) / 2"
      ],
      "metadata": {
        "id": "5hJZQW6Ark0h",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9e0f2eaf-70ce-4219-bd2a-3d774db5d7dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.865"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(0.85 * 13 + 0.88 * 17) / 30"
      ],
      "metadata": {
        "id": "PRQcS4SFropo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ea626f08-8277-4756-8274-a8d1bed1e1de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8669999999999999"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "acc = accuracy_score(y, y_pred1);print(acc)\n",
        "pre = precision_score(y, y_pred1);print(pre)\n",
        "rec = recall_score(y, y_pred1);print(rec)\n",
        "f1 = f1_score(y, y_pred1);print(f1)"
      ],
      "metadata": {
        "id": "jZBBbi6RiYkk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e4456497-3aba-4421-e960-01ad5bbca317"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8666666666666667\n",
            "0.8823529411764706\n",
            "0.8823529411764706\n",
            "0.8823529411764706\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "위 0번이 양성클래스\n",
        "\n",
        "[[11  2]\n",
        "\n",
        " [ 2 15]]"
      ],
      "metadata": {
        "id": "EJSH-VTHlzXA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# TP FN\n",
        "# FP TN"
      ],
      "metadata": {
        "id": "g5K9eKUUA2Fj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 정확도, 정밀도, 재현율, f1점수를 직접 계산하시오."
      ],
      "metadata": {
        "id": "H3BuLHtM4L3R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 정확도\n",
        "acc = (11 + 15) / (11 + 2 + 2 + 15);print(acc)"
      ],
      "metadata": {
        "id": "sfJyyG_plzXG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f01b440a-9efe-489f-ba62-277663548124"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8666666666666667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 정밀도 = 위 아래\n",
        "pre = 11 / (11 + 2);print(pre)"
      ],
      "metadata": {
        "id": "9E2xuMjylzXG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "811b665a-c7ed-4dc8-b3c9-74b402a9c3b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8461538461538461\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 재현율 = 좌 우\n",
        "rec = 11 / (11 + 2);print(rec)"
      ],
      "metadata": {
        "id": "zx9u_UkglzXG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "db1a3784-f157-4019-d347-1a7bf9a2527e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8461538461538461\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# f1 점수\n",
        "f1 = 2*pre*rec/(pre + rec);print(f1)"
      ],
      "metadata": {
        "id": "AVKlWt3clzXH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ee08676a-95a6-485b-f7d6-31df76a16de5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8461538461538461\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "아래 1번이 양성클래스\n",
        "\n",
        "[[11  2]\n",
        "\n",
        " [ 2 15]]"
      ],
      "metadata": {
        "id": "-pwDzxTlkVXn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# TN FP\n",
        "# FN TP"
      ],
      "metadata": {
        "id": "otar3ErsA8J0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 정확도, 정밀도, 재현율, f1점수를 직접 계산하시오."
      ],
      "metadata": {
        "id": "Ppq1s4tRAoCv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 정확도\n",
        "acc = (11 + 15) / (11 + 2 + 2 + 15);print(acc)"
      ],
      "metadata": {
        "id": "mHM3XDXSkYbG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "859e54d0-18b2-4fa5-d70a-4fe0177a8a37"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8666666666666667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 정밀도 = 위 아래\n",
        "pre = 15 / (15 + 2);print(pre)"
      ],
      "metadata": {
        "id": "Rm28MQDxk1sb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6d758f40-6aaa-4ef5-df2f-8121d253e8ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8823529411764706\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 재현율 = 좌 우\n",
        "rec = 15 / (15 + 2);print(rec)"
      ],
      "metadata": {
        "id": "a3cDyMr1lBAg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "41c4ee1b-4f54-40b3-fbd4-cfbce156e9f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8823529411764706\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# f1 점수\n",
        "f1 = 2*pre*rec/(pre + rec);print(f1)"
      ],
      "metadata": {
        "id": "I3i4sAqUlZsv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "14dcc04e-06d4-4886-c8c9-3fbb90ae43f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8823529411764706\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 문제풀이\n",
        "x1 = 20; x2 = 30\n",
        "zv = -9.31469 + 0.13407 * x1 + 0.17728 * x2\n",
        "print(round(expit(zv), 5))\n",
        "print(round(np.exp(zv) / (1 + np.exp(zv)),5))\n",
        "print(round(1 / (1 + np.exp(-zv)),5))"
      ],
      "metadata": {
        "id": "mZtsQ8i_lnYN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "187f7939-01a9-4b48-fe97-2e029af1f593"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.21167\n",
            "0.21167\n",
            "0.21167\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x1 = 30; x2 = 30\n",
        "zv = -9.31469 + 0.13407 * x1 + 0.17728 * x2\n",
        "print(round(expit(zv), 5))\n",
        "print(round(np.exp(zv) / (1 + np.exp(zv)),5))\n",
        "print(round(1 / (1 + np.exp(-zv)),5))"
      ],
      "metadata": {
        "id": "nj_R3R8VqYNI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8f071dee-b4e2-40f5-9570-a3520279a59d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.50645\n",
            "0.50645\n",
            "0.50645\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 선형확률모형"
      ],
      "metadata": {
        "id": "qOhjU6fbK95r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "id": "Sjh02Z-lsfw5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d8c92b3c-441b-48dd-ea31-b249d9ac8d8d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 30 entries, 0 to 29\n",
            "Data columns (total 3 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   y       30 non-null     int64  \n",
            " 1   x1      30 non-null     float64\n",
            " 2   x2      30 non-null     float64\n",
            "dtypes: float64(2), int64(1)\n",
            "memory usage: 848.0 bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 전체 데이터\n",
        "X = df.loc[:,['x1', 'x2']]\n",
        "y = df['y']\n",
        "\n",
        "print(\"X 데이터: \", X.shape)\n",
        "print(\"y 데이터: \", y.shape)"
      ],
      "metadata": {
        "id": "-hZ2O46hsfw6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "974b7b66-3ab8-44a8-b519-a13cffa0332b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X 데이터:  (30, 2)\n",
            "y 데이터:  (30,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 다중선형회귀모델\n",
        "from sklearn.linear_model import LinearRegression\n",
        "m2 = LinearRegression();m2.fit(X, y)"
      ],
      "metadata": {
        "id": "3FShHI0Tsfw7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "outputId": "a690b039-9865-4a4c-dcbc-02986e87f51c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 계수 = 모델 파라미터\n",
        "# 절편\n",
        "intercept2 = round(m2.intercept_, 5);print(intercept2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bd5c56c5-3e15-4bc3-de88-390b9b02b64f",
        "id": "zcNLHD9ILIoB"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-0.86815\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 기울기\n",
        "slope2 = [round(x, 4) for x in m2.coef_];print(slope2)"
      ],
      "metadata": {
        "id": "yokvTx0Tsfw8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c4986b68-5405-4f5f-dc4c-d8569f235c03"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.0188, 0.0258]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for name, coef in zip(X.columns, slope2):\n",
        "  print(\"{}: {}\".format(name, coef))"
      ],
      "metadata": {
        "id": "decGmBGmsfw9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2347e7ac-d73b-414c-99fc-ab961fa5de05"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x1: 0.0188\n",
            "x2: 0.0258\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 표본회귀식\n",
        "equation2 = \"y_hat = \" + str(intercept2)\n",
        "for coef, name in zip(slope2, X.columns):\n",
        "  equation2 += \" + {} * {}\".format(coef, name)\n",
        "print(equation2)"
      ],
      "metadata": {
        "id": "PPmDtLwFsfw9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "55b58be7-c974-47d1-a775-516d9edf5ff5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "y_hat = -0.86815 + 0.0188 * x1 + 0.0258 * x2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 예측값\n",
        "y_pred2 = m2.predict(X)\n",
        "y_pred2"
      ],
      "metadata": {
        "id": "hxcn4N_twk0E",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "22a82969-a01e-490f-976e-e31681522fb7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.7299,  1.2312,  0.8231,  1.0453,  0.3943,  0.7541,  1.0849,\n",
              "        0.7872,  0.5467,  0.5937,  0.4966,  0.1333,  0.0888,  0.294 ,\n",
              "        0.8069,  0.4381,  0.9597,  0.771 ,  1.0913,  0.292 , -0.0327,\n",
              "        0.0875,  1.0684,  0.2046,  0.5814,  0.2456,  0.2282,  0.3126,\n",
              "        0.7805,  0.162 ])"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 분류값\n",
        "y_bin = [np.round(x) for x in y_pred2]\n",
        "y_bin[:5]"
      ],
      "metadata": {
        "id": "0Mlih5aNwk0Z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5b5b7d41-d627-4baf-91da-819fef5f84e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.0, 1.0, 1.0, 1.0, 0.0]"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 성능 평가\n",
        "np.mean(y == y_bin)"
      ],
      "metadata": {
        "id": "tjQX6x9_wk0a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1b00bed0-8d4b-43dd-87fe-f24836e9d6ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8333333333333334"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 문제풀이\n",
        "x1 = 20; x2 = 30\n",
        "y_hat = -0.86815 + 0.0188 * x1 + 0.0258 * x2\n",
        "print(round(y_hat, 5))"
      ],
      "metadata": {
        "id": "vm8pLhZC6L-e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3051cf43-0c6a-439d-806d-1efb0c6a1f34"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.28185\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x1 = 30; x2 = 30\n",
        "y_hat = -0.86815 + 0.0188 * x1 + 0.0258 * x2\n",
        "print(round(y_hat, 5))"
      ],
      "metadata": {
        "id": "v9qNy22q6L-h",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "83511cb0-13c4-4018-8bbc-7791c165e5d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.46985\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x1 = [0,5,20,30,50,60]\n",
        "x2 = 30\n",
        "n = len(x1)\n",
        "x1_list = []\n",
        "x2_list = []\n",
        "lp_list = []\n",
        "logit_p_list = []\n",
        "for i in range(n):\n",
        "  lp = -0.86815 + 0.0188 * x1[i] + 0.0258 * x2\n",
        "  logit_y = -9.31469 + 0.13407 * x1[i] + 0.17728 * x2\n",
        "  logit_p = 1 / (1 + np.exp(-logit_y))\n",
        "  x1_list.append(x1[i])\n",
        "  x2_list.append(x2)\n",
        "  lp_list.append(lp)\n",
        "  logit_p_list.append(logit_p)\n",
        "\n",
        "prob_df = pd.DataFrame({'x1': x1_list,\n",
        "                        'x2': x2_list,\n",
        "                        'lp': lp_list,\n",
        "                        'logit_p': logit_p_list})\n",
        "prob_df"
      ],
      "metadata": {
        "id": "qHBnWzNz9bgn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "c647c7d4-ec18-446b-838f-50229b1291f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   x1  x2       lp   logit_p\n",
              "0   0  30 -0.09415  0.018052\n",
              "1   5  30 -0.00015  0.034692\n",
              "2  20  30  0.28185  0.211670\n",
              "3  30  30  0.46985  0.506452\n",
              "4  50  30  0.84585  0.937451\n",
              "5  60  30  1.03385  0.982841"
            ],
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "prob_df",
              "summary": "{\n  \"name\": \"prob_df\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"x1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24,\n        \"min\": 0,\n        \"max\": 60,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0,\n          5,\n          60\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"x2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 30,\n        \"max\": 30,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          30\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4517871180102417,\n        \"min\": -0.09414999999999996,\n        \"max\": 1.0338500000000002,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          -0.09414999999999996\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"logit_p\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4337194082279233,\n        \"min\": 0.01805185591961909,\n        \"max\": 0.9828407547871947,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.01805185591961909\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    }
  ]
}