LinearRegression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "LinearRegression.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMWhi61hts4LwOr8LaiztFh",
      "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/symaeng98/Artificial_Intelligence/blob/main/LinearRegression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jBZG4DKSYYtO"
      },
      "source": [
        "import numpy as np\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])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aVqp-U4sacVE"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "train_input, test_input, train_target, test_target = train_test_split(\n",
        "    perch_length, perch_weight, random_state = 42\n",
        ")\n",
        "train_input = train_input.reshape(-1,1)\n",
        "test_input = test_input.reshape(-1,1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a6B_TBSca9et",
        "outputId": "121054ad-2846-4052-8ba6-97dc6acf0fb5"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "\n",
        "knr = KNeighborsRegressor(n_neighbors=3)\n",
        "\n",
        "knr.fit(train_input, train_target)\n",
        "\n",
        "print(knr.predict([[50]]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1033.33333333]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EUEJvCyZ_YMh",
        "outputId": "91b536a4-d679-49b4-be4e-48beda9f281b"
      },
      "source": [
        "print(knr.predict([[50]]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1033.33333333]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I5ukACXVb2_n",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "acae4ff2-45f5-4109-aaf7-3fb7fb49bd02"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "distance, indexes = knr.kneighbors([[50]])\n",
        "\n",
        "plt.scatter(train_input, train_target)\n",
        "\n",
        "plt.scatter(train_input[indexes], train_target[indexes],marker='D')\n",
        "\n",
        "plt.scatter(50, 1033, marker = '^')\n",
        "plt.xlabel('length')\n",
        "plt.ylabel('weight')\n",
        "plt.show()\n",
        "\n",
        "##길이가 커질수록 무게가 증가하는 경향이 있는데, 새로운 샘플은 훈련세트의 범위를 벗어나서 엉뚱한 값이 나올 수 있다."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2A7UHerKeEna",
        "outputId": "6f938dfe-bf8a-49d6-bc28-3f5d786a5cd6"
      },
      "source": [
        "print(np.mean(train_target[indexes]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1033.3333333333333\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yu8B9e0BfVz5",
        "outputId": "e39107ef-c4fb-40dc-882e-a74b48192dbc"
      },
      "source": [
        "print(knr.predict([[100]])) ##K-이웃 회귀의 문제점이 드러난다. 가장 가까운 값을 평균내서 값을 도출하기때문에, input값이 아무리 커져도 최대값이 정해져있다."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1033.33333333]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HAtUMEbGfkNS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9c1f9298-c272-44f8-d6da-cdaa7f4c9492"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "lr = LinearRegression()\n",
        "\n",
        "lr.fit(train_input,train_target)\n",
        "\n",
        "print(lr.predict([[50]]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1241.83860323]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4q3b8s-KIsOs",
        "outputId": "579e8643-a4a6-4b88-e34e-057f0bca95c9"
      },
      "source": [
        "print(lr.predict([[50]]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1241.83860323]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jF6M5QAVmZfM",
        "outputId": "9f692090-c8b5-451d-bf18-9ac5ef5a46a7"
      },
      "source": [
        "print(lr.coef_, lr.intercept_) ##coef_ coefficient의 약자로 '계수'라는 뜻이다. 또는 가중치(weight)라고 불린다."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[39.01714496] -709.0186449535477\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "frqbccR7m3Sw"
      },
      "source": [
        "coef_와 intercept_ 는 머신러닝 알고리즘이 찾은 값이라는 의미로 '모델 파라미터'라고 부른다. 많은 머신러닝 알고리즘의 훈련 과정은 최적의 모델 파라미터를 찾는 것과 같다. 이를 **모델 기반 학습**이라고 한다. \n",
        "k-최근접 이웃같은 모델 파라미터가 없는 학습은 **사례 기반 학습**이라고 한다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1NCdeNotm2qe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "53b3b3b7-f3af-41d1-de54-4415f110e65b"
      },
      "source": [
        "plt.scatter(train_input, train_target)\n",
        "\n",
        "plt.plot([15,50], [15*lr.coef_ + lr.intercept_, 50*lr.coef_ + lr.intercept_]) #두 점을 이어준다. x 값과 y값은 각각 따로 한 리스트에 넣어준다.\n",
        "\n",
        "plt.scatter(50, 1241.8, marker='^')\n",
        "plt.xlabel('length')\n",
        "plt.ylabel('weight')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S8Pvfx9WprOz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6a6841c9-6314-4336-e7a4-c7ec99a78e2b"
      },
      "source": [
        "print(lr.score(train_input, train_target))#과대적합되었다고 볼 수 있을까? 훈련 세트의 점수도 높지 않다. 오히려 전체적으로 과소적합 되었다고 볼 수 있다.\n",
        "print(lr.score(test_input, test_target))#과소적합 외에도 x 값이 작을 때 오차가 크다."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9398463339976039\n",
            "0.8247503123313558\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xNHPfr_98keP",
        "outputId": "b1d9b810-22b1-43d9-8672-4fdc4d470339"
      },
      "source": [
        "train_poly = np.column_stack((train_input ** 2, train_input)) ##column_stack은 두 배열을 일렬로 나열하여 붙여준다.\n",
        "test_poly = np.column_stack((test_input ** 2, test_input))\n",
        "\n",
        "lr = LinearRegression()\n",
        "lr.fit(train_poly, train_target)\n",
        "\n",
        "print(lr.predict([[50**2, 50]]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1573.98423528]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lKG17lYsGGfc",
        "outputId": "eba29ba7-aece-47f7-b5ac-61e3fc8ff8ae"
      },
      "source": [
        "print(lr.coef_,lr.intercept_) #무게 = 1.01 * 길이^2 - 21.6 * 길이 + 116.0502~ 라는 뜻이다."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[  1.01433211 -21.55792498] 116.05021078278276\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "CJ_3k4opFouH",
        "outputId": "cd7931e5-3c71-4920-fefd-cca382714109"
      },
      "source": [
        "point = np.arange(15,50) #15~49까지 정수 배열 생성\n",
        "\n",
        "plt.scatter(train_input, train_target)\n",
        "\n",
        "plt.plot(point, 1.01*point**2 - 21.6*point + 116.05)\n",
        "\n",
        "plt.scatter(50, 1574, marker='^')\n",
        "plt.xlabel('length')\n",
        "plt.ylabel('weight')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dZGGMYwuXjlG",
        "outputId": "84e114e9-182c-4177-9ada-6ce06db11583"
      },
      "source": [
        "print(lr.score(train_poly, train_target))\n",
        "print(lr.score(test_poly, test_target))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9706807451768623\n",
            "0.9775935108325122\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}