LogisticRegression.ipynb

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              "  Species  Weight  Length  Diagonal   Height   Width\n",
              "0   Bream   242.0    25.4      30.0  11.5200  4.0200\n",
              "1   Bream   290.0    26.3      31.2  12.4800  4.3056\n",
              "2   Bream   340.0    26.5      31.1  12.3778  4.6961\n",
              "3   Bream   363.0    29.0      33.5  12.7300  4.4555\n",
              "4   Bream   430.0    29.0      34.0  12.4440  5.1340"
            ],
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              "      <th></th>\n",
              "      <th>Species</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Length</th>\n",
              "      <th>Diagonal</th>\n",
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              "      <td>Bream</td>\n",
              "      <td>242.0</td>\n",
              "      <td>25.4</td>\n",
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              "      <td>4.0200</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Bream</td>\n",
              "      <td>290.0</td>\n",
              "      <td>26.3</td>\n",
              "      <td>31.2</td>\n",
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              "      <th>2</th>\n",
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              "      <th>3</th>\n",
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              "      <td>29.0</td>\n",
              "      <td>34.0</td>\n",
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          },
          "metadata": {},
          "execution_count": 86
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "fish = pd.read_csv('https://bit.ly/fish_csv_data')\n",
        "fish.head()\n",
        "# pandas의 데이터 프레임"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# unique() 함수를 사용하여 Species 열에서 고유한 값을 추출\n",
        "print(pd.unique(fish['Species']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fhzyQBDuwn49",
        "outputId": "a33304b6-90a5-4df0-fbec-8812304e3a80"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Bream' 'Roach' 'Whitefish' 'Parkki' 'Perch' 'Pike' 'Smelt']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Species 열을 타깃으로 만들고 나머지 5개 열을 입력 프레임으로 사용\n",
        "fish_input = fish[['Weight', 'Length', 'Diagonal', 'Height', 'Width']].to_numpy()\n",
        "print(fish_input[:5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ylkKKYf4w7N7",
        "outputId": "a0ca11c4-1e69-472b-c5d7-19489e20d32b"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[242.      25.4     30.      11.52     4.02  ]\n",
            " [290.      26.3     31.2     12.48     4.3056]\n",
            " [340.      26.5     31.1     12.3778   4.6961]\n",
            " [363.      29.      33.5     12.73     4.4555]\n",
            " [430.      29.      34.      12.444    5.134 ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fish_target = fish['Species'].to_numpy()"
      ],
      "metadata": {
        "id": "qUIEyjvvxVBa"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train_input, test_input, train_target, test_target = train_test_split(fish_input, fish_target, random_state=42)"
      ],
      "metadata": {
        "id": "URhzw0kfxXd1"
      },
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "# 표준화 전처리하기\n",
        "\n",
        "ss = StandardScaler()\n",
        "ss.fit(train_input)\n",
        "train_scaled = ss.transform(train_input)\n",
        "test_scaled = ss.transform(test_input)"
      ],
      "metadata": {
        "id": "JRZdIAryyC1a"
      },
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# K-최근접 이웃 분류기의 확률 예측\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "kn = KNeighborsClassifier(n_neighbors=3)\n",
        "kn.fit(train_scaled, train_target)\n",
        "print(kn.score(train_scaled, train_target))\n",
        "print(kn.score(test_scaled, test_target))\n",
        "# 점수가 둘 다 낮기 때문에 과소적합인듯\n",
        "# 클래스 확률을 배우기 때문에 신경쓰지 않고 넘어감"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ICU4POBvyYEo",
        "outputId": "916eea5c-8038-4ce7-d6e3-3c0ec4ae42ac"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8907563025210085\n",
            "0.85\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 물고기 종이 7종류가 들어갔다. 타깃 데이터에 2개 이상의 클래스가 포함된 문제를 다중 분류라고 한다.\n",
        "# 이진 분류와 모델을 만들고 훈련하는 방식은 동일\n",
        "# 이진 분류 -> 양성클래스 1 음성클래스 0\n",
        "# 다중분류 -> 타깃값을 숫자로 변경 가능하지만 사이킷런에서는 편리하게 문자열로 된 타깃값을 그대로 사용 가능\n",
        "# 타깃값을 그대로 사이킷런 모델에 전달하면 순서가 자동으로 알파벳 순으로 매겨짐 -> pd.unique(fish['Species])로 출력된 순서와 다름\n",
        "print(kn.classes_) # 정렬된 타깃값 정보를 볼 수 있음"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bRN9gOimy0IJ",
        "outputId": "89782675-80a5-4fc2-8668-299dbfbf9759"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Bream' 'Parkki' 'Perch' 'Pike' 'Roach' 'Smelt' 'Whitefish']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(kn.predict(test_scaled[:5])) # 처음 5개 샘플의 타깃값을 예측"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GXPwWjF4z4OE",
        "outputId": "7982ff26-f6ed-4db9-f83b-0e2f4eab7381"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Perch' 'Smelt' 'Pike' 'Perch' 'Perch']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 5개 샘플에 대한 예측은 어떤 확률로 만들어졌나?\n",
        "# predict_proba() 메서드로 클래스별 확률값을 반환\n",
        "# 넘파이의 round() 메서드로 클래스별 확률값을 반환\n",
        "# round()는 기본적으로 소수점 첫째 자리에서 반올림 -> decimals 매개변수로 자릿수 지정가능\n",
        "import numpy as np\n",
        "\n",
        "proba = kn.predict_proba(test_scaled[:5])\n",
        "print(np.round(proba, decimals=4))\n",
        "# 각 샘플에 대한 확률이 나온다.\n",
        "# 순서는 위에서 classes값과 같다"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uSVrmFL-6ACT",
        "outputId": "daa6ed4d-0818-475d-b155-2dc87e3b7c5c"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.     0.     1.     0.     0.     0.     0.    ]\n",
            " [0.     0.     0.     0.     0.     1.     0.    ]\n",
            " [0.     0.     0.     1.     0.     0.     0.    ]\n",
            " [0.     0.     0.6667 0.     0.3333 0.     0.    ]\n",
            " [0.     0.     0.6667 0.     0.3333 0.     0.    ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "distances, indexes = kn.kneighbors(test_scaled[3:4])\n",
        "# kneighbors 메서드는 2차원 배열을 입력 -> 넘파이 배열의 슬라이싱 연산자를 사용하여 2차원 배열로 자동으로 생성\n",
        "print(train_target[indexes])\n",
        "# 가까운 이웃을 출력 -> Roach 1개 Perch 2개 위의 확률과 동일하게 나타난다.\n",
        "# predict_proba를 이용하여 간단하게 예측 가능하다.\n",
        "# 3개의 최근접 이웃을 사용했으므로 0/3, 1/3, 2/3, 3/3의 확률로만 표현이 된다.\n",
        "# 더 좋은 방법으로 로지스틱 회귀를 사용"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "czeOciTe6i0f",
        "outputId": "40ee9bac-995b-470d-8c97-e76d915a34b3"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[['Roach' 'Perch' 'Perch']]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 로지스틱 회귀는 이름이 회귀, 하지만 분류 모델\n",
        "# 선형회귀와 동일하게 선형 방정식을 학습\n",
        "# z = a x weight + b x length + c x diagonal + d x height + e x width + f\n",
        "# a, b, c, d, e = 가중치 혹은 계수\n",
        "# z = 어떤 값도 가능 -> 확률이 되기 위해 0 ~ 1 사이의 값이 되어야함\n",
        "# z = 아주 큰 음수일때 0 아주 큰 양수일때 1로 바꿔야함 -> 시그모이드 함수(로지스틱 함수라고도 함)\n",
        "# S(z) = 1/1+e^(-z) = e^x/e^z+1\n",
        "# 선형 방정식의 출력 z의 음수를 사용해 자연 상수 e를 거듭제곱하고 1을 더한 값의 역수를 취함\n",
        "# 음수: 0, 0: 0.5, 양수: 1 -> 퍼센트 확률로 해석 가능\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# -5, 5 사이에 0.1 간격으로 배열 z를 생성\n",
        "# z의 위치마다 시그모이드 함수 계산\n",
        "# 지수 함수 계산은 np.exp() 함수\n",
        "z = np.arange(-5, 5, 0.1)\n",
        "phi = 1/(1 + np.exp(-z))\n",
        "plt.plot(z, phi)\n",
        "plt.xlabel('z')\n",
        "plt.ylabel('phi')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "U-7PfZ1x7wvz",
        "outputId": "02840e43-2544-40d9-f1e8-ecba8c3b2971"
      },
      "execution_count": 97,
      "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": [
        "# 이진 분류 수행\n",
        "# 시그모이드 함수의 출력이 0.5보다 크면 양성클래스, 0.5보다 작으면 음성클래스\n",
        "# 도미와 빙어의 이진분류\n",
        "char_arr = np.array(['A', 'B', 'C', 'D', 'E'])\n",
        "print(char_arr[[True, False, True, False, False]])\n",
        "# 넘파이 배열은 True, False 값을 전달하여 행을 선택할 수 있음\n",
        "# 불리언 인덱싱이라고 함"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qqgee-gH-b2w",
        "outputId": "52d41c7c-240d-476d-c0cc-3c66ab275a27"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['A' 'C']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 비교연산자를 이용하여 도미와 빙어를 True로 판단하게 끔 만듬\n",
        "bream_smelt_indexes = (train_target == 'Bream') | (train_target == 'Smelt')\n",
        "train_bream_smelt = train_scaled[bream_smelt_indexes]\n",
        "target_bream_smelt = train_target[bream_smelt_indexes]"
      ],
      "metadata": {
        "id": "tzBaX-kGJjtY"
      },
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 위의 데이터를 이용하여 로지스틱 회귀 모델을 훈련\n",
        "# LogisticRegression 클래스는 선형 모델 -> sklearn.linear_model 패키지에 있음\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "lr = LogisticRegression()\n",
        "lr.fit(train_bream_smelt, target_bream_smelt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "4DY2cDhhKDd9",
        "outputId": "a468b0bc-9e58-46fd-9b59-846c60a189e8"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "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>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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" 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": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 훈련 모델을 이용하여 train_bream_smelt에 있는 처음 5개 샘플 예측\n",
        "print(lr.predict(train_bream_smelt[:5]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BWZx8N6cKe18",
        "outputId": "bd6e81be-a175-4a38-d3fa-aa85fe2d7fe8"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Bream' 'Smelt' 'Bream' 'Bream' 'Bream']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 5개 샘플의 예측 확률 출력\n",
        "print(lr.predict_proba(train_bream_smelt[:5]))\n",
        "# 첫번째 열이 0 두번째 열이 1\n",
        "print(lr.classes_)\n",
        "# 첫번째가 Bream -> 도미는 음성클래스, 빙어는 양성클래스"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yqFQD-SJLMDP",
        "outputId": "b4696b3d-e4f4-4424-f825-0f7beedac5c1"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.99759855 0.00240145]\n",
            " [0.02735183 0.97264817]\n",
            " [0.99486072 0.00513928]\n",
            " [0.98584202 0.01415798]\n",
            " [0.99767269 0.00232731]]\n",
            "['Bream' 'Smelt']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 로지스틱 회귀가 학습한 계수 출력\n",
        "print(lr.coef_, lr.intercept_)\n",
        "# z = -0.404 x weight + -0.576 x length + -0.663 x diagonal + 1.013 x height + 0.732 x width + 2.161\n",
        "# 선형 회귀와 상당히 비슷"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PY0veUmtLUGp",
        "outputId": "54cb231c-fe58-4ce2-dc0c-e68520288c3c"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-0.4037798  -0.57620209 -0.66280298 -1.01290277 -0.73168947]] [-2.16155132]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# LogisticRegression 모델로 z값 계산\n",
        "decisions = lr.decision_function(train_bream_smelt[:5])\n",
        "print(decisions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cAK5I8rnLz3A",
        "outputId": "3638c4fd-4e3b-4a72-b6c6-d8598b9e35ba"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-6.02927744  3.57123907 -5.26568906 -4.24321775 -6.0607117 ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 위의 z값을 시그모이드 함수에 통과시키면 확률을 얻을 수 있음\n",
        "# 파이썬 사이파이 라이브러리의 시그모이드 함수 expit() 사용 -> np.exp() 함수보다 편리하고 안전\n",
        "from scipy.special import expit\n",
        "\n",
        "print(expit(decisions))\n",
        "# 이진 분류를 위해 2개의 생선 샘플을 골라냈고 이를 사용해 로지스틱 회귀 모델을 훈련\n",
        "# 이진 분류일 경우 predict() 메서드는 음성 클래스와 양성 클래스에 대한 확률을 출력\n",
        "# decision_function() 메서드는 양성 클래스에 대한 z값을 계산\n",
        "# coef_ 속성과 intercept_ 속성에는 로지스킥 모델이 학습한 선형 방정식의 계수가 들어있음"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2sHX_JuSNFkb",
        "outputId": "9e2bb510-a80c-4bcb-efd8-d619f933fb84"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.00240145 0.97264817 0.00513928 0.01415798 0.00232731]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 로지스틱 회귀로 다중 분류 수행\n",
        "# 이진분류와 다중분류의 차이\n",
        "# LogisticRegression 클래스는 기본적인 반복 알고리즘 사용\n",
        "# max_iter 매개변수에서 반복 횟수를 지정 (기본값 100)\n",
        "# LogisticRegression은 기본적으로 릿지 회귀와 같이 계수의 제곱을 규제 -> L2 규제라고 부름\n",
        "# 릿지 회귀에서는 alpha 매개변수로 규제의 양 조절, 증가시 규제도 커짐\n",
        "# LogisticRegression에서 규제 제어 매개변수는 C, 감소시 규제가 커짐, 기본값 1\n",
        "# 규제 완화를 위해 20, 반복 횟수를 증가시키기 위해 1000\n",
        "lr = LogisticRegression(C=20, max_iter=1000)\n",
        "lr.fit(train_scaled, train_target)\n",
        "print(lr.score(train_scaled, train_target))\n",
        "print(lr.score(test_scaled, test_target))\n",
        "# 둘의 점수가 높고 과대적합, 과소적합이 발생하지 않음"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oNgpJ2AqNeXS",
        "outputId": "a3b1e81f-753c-4b0f-a937-3d30e1033a03"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9327731092436975\n",
            "0.925\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 테스트 세트 처음 5개에 대한 예측\n",
        "print(lr.predict(test_scaled[:5]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jx4KXyLjPR18",
        "outputId": "a9fbe5d6-651f-4553-800c-c6630545197f"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Perch' 'Smelt' 'Pike' 'Roach' 'Perch']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 예측에 대한 확률\n",
        "proba = lr.predict_proba(test_scaled[:5])\n",
        "print(np.round(proba, decimals=3))\n",
        "print(lr.classes_)\n",
        "# 다중 분류도 이진 분류와 다를 바 없음\n",
        "# 각 클래스 개수만큼 확률을 출력"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mDzUXpmSPfok",
        "outputId": "0e00d27f-b6d9-449f-8290-e223185e529d"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.    0.014 0.841 0.    0.136 0.007 0.003]\n",
            " [0.    0.003 0.044 0.    0.007 0.946 0.   ]\n",
            " [0.    0.    0.034 0.935 0.015 0.016 0.   ]\n",
            " [0.011 0.034 0.306 0.007 0.567 0.    0.076]\n",
            " [0.    0.    0.904 0.002 0.089 0.002 0.001]]\n",
            "['Bream' 'Parkki' 'Perch' 'Pike' 'Roach' 'Smelt' 'Whitefish']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 선형방정식 출력\n",
        "print(lr.coef_.shape, lr.intercept_.shape)\n",
        "# 5개의 특성을 사용하므로 열은 5개 행은 7, 행이 좀 이상한데? -> 이진 분류에서 보았던 z를 7개나 계산한다는 의미\n",
        "# 이진분류에서는 시그모이드 함수를 사용했지만, 다중분류에서는 소프트맥스 함수를 사용한다."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mYJWznVqPtMw",
        "outputId": "cba28cbd-4ea7-451f-83a8-13dc378e3c8c"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(7, 5) (7,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 소프트맥스 함수는 여러개의 선형 방정식의 출력값을 0 ~ 1사이로 압축하고 전체 합이 1이 되도록 만든다.\n",
        "# 이를 위해 지수함수를 사용하기 때문에 정규화된 지수함수라고도 부른다.\n",
        "# e_sum = e^z1 + e^z2 + ... + e^z7\n",
        "# 양 변을 e_sum으로 나눠주면 총 합이 1이 되게 된다.\n",
        "# s1 = e^z1/e_sum, ...\n",
        "decision = lr.decision_function(test_scaled[:5])\n",
        "print(np.round(decision, decimals=2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UEtenfsMQD4-",
        "outputId": "a993efec-abba-450e-dc8d-ff010f960060"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ -6.5    1.03   5.16  -2.73   3.34   0.33  -0.63]\n",
            " [-10.86   1.93   4.77  -2.4    2.98   7.84  -4.26]\n",
            " [ -4.34  -6.23   3.17   6.49   2.36   2.42  -3.87]\n",
            " [ -0.68   0.45   2.65  -1.19   3.26  -5.75   1.26]\n",
            " [ -6.4   -1.99   5.82  -0.11   3.5   -0.11  -0.71]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.special import softmax\n",
        "\n",
        "proba = softmax(decision, axis=1)\n",
        "print(np.round(proba, decimals=3))\n",
        "# 각 클래스에 맞게 확률이 나온다."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E_xAf2t4RAYv",
        "outputId": "ea2b8bd3-2f5b-41bd-864f-064d2cf28c94"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.    0.014 0.841 0.    0.136 0.007 0.003]\n",
            " [0.    0.003 0.044 0.    0.007 0.946 0.   ]\n",
            " [0.    0.    0.034 0.935 0.015 0.016 0.   ]\n",
            " [0.011 0.034 0.306 0.007 0.567 0.    0.076]\n",
            " [0.    0.    0.904 0.002 0.089 0.002 0.001]]\n"
          ]
        }
      ]
    }
  ]
}