PerchLengthWeight

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPyTuE409RiVwAGdDU93VF6",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/CocoPig1028/ML_Note/blob/main/PerchLengthWeight\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "dG7-3jkjekzs"
      },
      "outputs": [],
      "source": [
        "# 농어의 길이를 기반으로 무게 예측하기\n",
        "# 샘플은 농어의 길이, 타겟은 농어의 무게가 되겠구먼\n",
        "# 여기서는 농어의 훈련세트를 통해 나온 결정계수로 테스트세트의 결정계수를 구하여 정확도를 나타내었다.\n",
        "# 과대적합, 과소적합을 데이터 셋의 크기가 작기 때문에 결정하는 이웃의 수를 줄여 적합도를 올렸다.\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "perch_length = np.array([8.4, 13.7, 15.0, 16.2, 17.4, 18.0, 18.7, 19.0, 19.6, 20.0, 21.0,\n",
        "       21.0, 21.0, 21.3, 22.0, 22.0, 22.0, 22.0, 22.0, 22.5, 22.5, 22.7,\n",
        "       23.0, 23.5, 24.0, 24.0, 24.6, 25.0, 25.6, 26.5, 27.3, 27.5, 27.5,\n",
        "       27.5, 28.0, 28.7, 30.0, 32.8, 34.5, 35.0, 36.5, 36.0, 37.0, 37.0,\n",
        "       39.0, 39.0, 39.0, 40.0, 40.0, 40.0, 40.0, 42.0, 43.0, 43.0, 43.5,\n",
        "       44.0])\n",
        "perch_weight = np.array([5.9, 32.0, 40.0, 51.5, 70.0, 100.0, 78.0, 80.0, 85.0, 85.0, 110.0,\n",
        "       115.0, 125.0, 130.0, 120.0, 120.0, 130.0, 135.0, 110.0, 130.0,\n",
        "       150.0, 145.0, 150.0, 170.0, 225.0, 145.0, 188.0, 180.0, 197.0,\n",
        "       218.0, 300.0, 260.0, 265.0, 250.0, 250.0, 300.0, 320.0, 514.0,\n",
        "       556.0, 840.0, 685.0, 700.0, 700.0, 690.0, 900.0, 650.0, 820.0,\n",
        "       850.0, 900.0, 1015.0, 820.0, 1100.0, 1000.0, 1100.0, 1000.0,\n",
        "       1000.0])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.scatter(perch_length, perch_weight)\n",
        "plt.xlabel('length')\n",
        "plt.ylabel('weight')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "hoX-fnw-fGW1",
        "outputId": "c196db00-9cab-4143-8286-c134383a86cf"
      },
      "execution_count": 2,
      "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": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train_input, test_input, train_target, test_target = train_test_split(perch_length, perch_weight, random_state=42)"
      ],
      "metadata": {
        "id": "oH3lKQmofZIK"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_array = np.array([1,2,3,4])\n",
        "print(test_array.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4igzTaWxgDpk",
        "outputId": "91a938e8-48d6-4d3d-9691-24c7c1f1ee22"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(4,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_array = test_array.reshape(2,2)\n",
        "print(test_array.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UI0mVtZ9gvw7",
        "outputId": "438c5a26-f6c5-46fc-9fd9-7942f3f9da3b"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_input = train_input.reshape(-1, 1)\n",
        "test_input = test_input.reshape(-1,1)\n",
        "print(train_input.shape, test_input.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "talvB1n8hS_-",
        "outputId": "59bb6fe1-d52d-4b45-811b-ede928c0cbff"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(42, 1) (14, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "\n",
        "knr = KNeighborsRegressor()\n",
        "\n",
        "# k-최근접 이웃 회귀 모델을 훈련합니다.\n",
        "knr.fit(train_input, train_target)\n",
        "\n",
        "print(knr.score(test_input, test_target))\n",
        "# 정확도 점수 출력\n",
        "# 정답을 맞힌 개수의 비율\n",
        "# 회귀의 경우 조금 다른 값으로 평가 -> 결정계수\n",
        "# 결정계수: R^2 = 1 - (타깃 - 예측)^2의 합 / (타깃 - 평균)^2의 합\n",
        "# 타깃의 평균 정도를 예측하는 수준: 0\n",
        "# 예측이 타깃에 아주 가까워지는 수준: 1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T6S_iRRiiLV0",
        "outputId": "62dfd477-eb4f-4fcc-dcad-e4421fd33ea2"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.992809406101064\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_absolute_error\n",
        "\n",
        "# 테스트 세트에 대한 예측을 만듭니다.\n",
        "test_prediction = knr.predict(test_input)\n",
        "\n",
        "# 테스트 세트에 대한 평균 절댓값 오차를 계산합니다.\n",
        "mae = mean_absolute_error(test_target, test_prediction)\n",
        "print(mae)\n",
        "\n",
        "# 훈련한 모델을 사용해 훈련 세트의 R^2 점수\n",
        "print(knr.score(train_input, train_target))\n",
        "# 훈련세트 점수보다 테스트세트 점수가 나쁘면 과대적합\n",
        "# 훈련세트 점수보다 테스트세트 점수가 좋다면 과소적합 -> 원인 중 하나가 훈련세트와 테스트 세트의 크기가 매우 작기 때문\n",
        "# 두 점수 모두 낮아도 과소적합\n",
        "# 여기서는 과소적합이 났음"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T6JvU2iqi741",
        "outputId": "a95da551-9b0d-4660-d77c-7731c95c2036"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "19.157142857142862\n",
            "0.9698823289099254\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 이웃의 개수를 3으로 설정합니다.\n",
        "knr.n_neighbors = 3\n",
        "\n",
        "# 모델을 다시 훈련\n",
        "knr.fit(train_input, train_target)\n",
        "print(knr.score(train_input, train_target))\n",
        "# 훈련 세트의 결정계수가 높아짐\n",
        "\n",
        "print(knr.score(test_input, test_target))\n",
        "# 테스트 세트의 점수는 훈련 세트보다 낮다 -> 과소적합 해결\n",
        "# 두 점수차가 크지 않으므로 과대적합도 아닌듯"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pN1Dc4Y2mcP1",
        "outputId": "c0d33439-3b8a-447c-c26c-23177f04087a"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9804899950518966\n",
            "0.9746459963987609\n"
          ]
        }
      ]
    }
  ]
}