3_회귀_알고리즘과_모델_규제.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOIBQYJlPuQkJytHGYVJTTF",
      "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/SIDED00R/hg_ml/blob/main/3_%ED%9A%8C%EA%B7%80_%EC%95%8C%EA%B3%A0%EB%A6%AC%EC%A6%98%EA%B3%BC_%EB%AA%A8%EB%8D%B8_%EA%B7%9C%EC%A0%9C.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 회귀"
      ],
      "metadata": {
        "id": "kDdf0AEfs6RK"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "np5j0UTtJNI_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "6b72315a-6808-49e2-9f66-b1b7543feb7e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "perch_length = np.array(\n",
        "    [8.4, 13.7, 15.0, 16.2, 17.4, 18.0, 18.7, 19.0, 19.6, 20.0,\n",
        "     21.0, 21.0, 21.0, 21.3, 22.0, 22.0, 22.0, 22.0, 22.0, 22.5,\n",
        "     22.5, 22.7, 23.0, 23.5, 24.0, 24.0, 24.6, 25.0, 25.6, 26.5,\n",
        "     27.3, 27.5, 27.5, 27.5, 28.0, 28.7, 30.0, 32.8, 34.5, 35.0,\n",
        "     36.5, 36.0, 37.0, 37.0, 39.0, 39.0, 39.0, 40.0, 40.0, 40.0,\n",
        "     40.0, 42.0, 43.0, 43.0, 43.5, 44.0]\n",
        "     )\n",
        "perch_weight = np.array(\n",
        "    [5.9, 32.0, 40.0, 51.5, 70.0, 100.0, 78.0, 80.0, 85.0, 85.0,\n",
        "     110.0, 115.0, 125.0, 130.0, 120.0, 120.0, 130.0, 135.0, 110.0,\n",
        "     130.0, 150.0, 145.0, 150.0, 170.0, 225.0, 145.0, 188.0, 180.0,\n",
        "     197.0, 218.0, 300.0, 260.0, 265.0, 250.0, 250.0, 300.0, 320.0,\n",
        "     514.0, 556.0, 840.0, 685.0, 700.0, 700.0, 690.0, 900.0, 650.0,\n",
        "     820.0, 850.0, 900.0, 1015.0, 820.0, 1100.0, 1000.0, 1100.0,\n",
        "     1000.0, 1000.0]\n",
        "     )\n",
        "\n",
        "plt.scatter(perch_length, perch_weight)\n",
        "plt.xlabel('length')\n",
        "plt.ylabel('weight')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "dqSDbM-K4pkB"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\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) # 2차원 배열로 만들기\n",
        "test_input = test_input.reshape(-1, 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "BcPh-Da44lhx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6b412682-c8ae-4db0-abc3-cd4c9f840336"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9698823289099254\n",
            "0.992809406101064\n"
          ]
        }
      ],
      "source": [
        "from sklearn.neighbors import KNeighborsRegressor # K-NN 회귀 모델\n",
        "\n",
        "knr = KNeighborsRegressor()\n",
        "knr.fit(train_input, train_target)\n",
        "print(knr.score(train_input, train_target)) # train의 예측이 더 않좋음으로 과소 적합\n",
        "print(knr.score(test_input, test_target))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pLW8kdDv5asl"
      },
      "source": [
        "### 과대적합 vs 과소적합"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jhu9abILLHjq",
        "outputId": "ed37c7ca-17ef-4d7c-e6ac-d4566c9cf3e5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9804899950518966\n",
            "0.9746459963987609\n"
          ]
        }
      ],
      "source": [
        "knr.n_neighbors = 3\n",
        "knr.fit(train_input, train_target)\n",
        "print(knr.score(train_input, train_target)) # 과소 적합 해결\n",
        "print(knr.score(test_input, test_target))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CgQlTY9VpWeb"
      },
      "source": [
        "### K-NN의 한계"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "knr.predict([[50]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pHMr8-UkO2ht",
        "outputId": "de6987ec-55e7-4615-900d-267e8ceb7056"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1033.33333333])"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "wR9eDDH2q9nP",
        "outputId": "ec21e1d8-5738-481c-fe73-074493c02ece"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "distances, indexes = knr.kneighbors([[50]])\n",
        "plt.scatter(train_input, train_target)\n",
        "plt.scatter(train_input[indexes], train_target[indexes], marker='D')\n",
        "plt.scatter(50, 1033.3, marker='^')\n",
        "plt.xlabel('length')\n",
        "plt.ylabel('weight')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8cw87IL3t_3p",
        "outputId": "13c38627-abe7-45ff-ad9a-90d978e06942"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1033.33333333])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "knr.predict([[100]])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "YIMtpZKPfQjc",
        "outputId": "75671089-1bdc-478e-f40f-87d8eed0c354"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "distances, indexes = knr.kneighbors([[100]])\n",
        "plt.scatter(train_input, train_target)\n",
        "plt.scatter(train_input[indexes], train_target[indexes], marker='D')\n",
        "plt.scatter(100, 1033, marker='^')\n",
        "plt.xlabel('length')\n",
        "plt.ylabel('weight')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yFadoVqdpe93"
      },
      "source": [
        "### 선형 회귀"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "AKnGV8g5uN_U",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f04f0b7a-3b8b-4338-8d84-501a14bbae68"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.939846333997604\n",
            "0.8247503123313558\n"
          ]
        }
      ],
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "lr = LinearRegression()\n",
        "lr.fit(train_input, train_target)\n",
        "print(lr.score(train_input, train_target))\n",
        "print(lr.score(test_input, test_target))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr.predict([[50]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ctM7PEbwRbc7",
        "outputId": "b7d7316d-d63f-4a07-c159-c50414ec0710"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1241.83860323])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "VumOdzlFuV_5",
        "outputId": "a3990da4-8d47-4c39-fe1f-bb691f8d01eb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.scatter(train_input, train_target)\n",
        "plt.plot([15, 50], [15*lr.coef_+lr.intercept_, 50*lr.coef_+lr.intercept_])\n",
        "plt.scatter(50, 1241.8, marker='^')\n",
        "plt.xlabel('length')\n",
        "plt.ylabel('weight')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr.predict([[100]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tnVpmq-QS8lc",
        "outputId": "c3ea7b6d-1634-480a-8923-c6f90c91d400"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3192.69585141])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(train_input, train_target)\n",
        "plt.plot([15, 100], [15*lr.coef_+lr.intercept_, 100*lr.coef_+lr.intercept_])\n",
        "plt.scatter(100, 3192.7, marker='^')\n",
        "plt.xlabel('length')\n",
        "plt.ylabel('weight')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "L1nRfnb7TDwV",
        "outputId": "65f6c2a6-0208-4361-bf90-f3ca2f9b9443"
      },
      "execution_count": 13,
      "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",
      "metadata": {
        "id": "h-akkHaaQc0b"
      },
      "source": [
        "### 다항 회귀"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "wcdsYFxB2q82",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "21526085-21dd-4b82-a694-37366d265871"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9706807451768623\n",
            "0.9775935108325122\n"
          ]
        }
      ],
      "source": [
        "train_poly = np.column_stack((train_input ** 2, train_input))\n",
        "test_poly = np.column_stack((test_input ** 2, test_input))\n",
        "lr = LinearRegression()\n",
        "lr.fit(train_poly, train_target)\n",
        "\n",
        "print(lr.score(train_poly, train_target))\n",
        "print(lr.score(test_poly, test_target))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LTlN32tML4Kf",
        "outputId": "bd4851e4-7e0c-4cbc-f2a0-8595993819b3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1573.98423528]\n"
          ]
        }
      ],
      "source": [
        "print(lr.predict([[50**2, 50]]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "c91qVYoPLz1H",
        "outputId": "c8975a47-484b-42e8-f028-6500b3d88c1b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "point = np.arange(15, 50)\n",
        "plt.scatter(train_input, train_target)\n",
        "plt.plot(point, lr.coef_[0]*point**2 + lr.coef_[1]*point + lr.intercept_)\n",
        "plt.scatter([50], [1574], marker='^')\n",
        "plt.xlabel('length')\n",
        "plt.ylabel('weight')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(lr.predict([[100**2, 100]]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C3MNX958UKSD",
        "outputId": "5e6ff784-784e-4ce5-fd4a-e3d50d3199aa"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[8103.57880667]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "point = np.arange(15, 100)\n",
        "plt.scatter(train_input, train_target)\n",
        "plt.plot(point, lr.coef_[0]*point**2 + lr.coef_[1]*point + lr.intercept_)\n",
        "plt.scatter([100], [8103.6], marker='^')\n",
        "plt.xlabel('length')\n",
        "plt.ylabel('weight')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "6yJRJkeGUKJd",
        "outputId": "9267f387-04d6-42e6-c015-5930800425b6"
      },
      "execution_count": 18,
      "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",
      "metadata": {
        "id": "fZwhQU2l8tI6"
      },
      "source": [
        "### 데이터 준비"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "3kjaTfOqEVwY"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('https://bit.ly/perch_csv_data')\n",
        "perch_full = df.to_numpy()\n",
        "perch_weight = np.array(\n",
        "    [5.9, 32.0, 40.0, 51.5, 70.0, 100.0, 78.0, 80.0, 85.0, 85.0,\n",
        "     110.0, 115.0, 125.0, 130.0, 120.0, 120.0, 130.0, 135.0, 110.0,\n",
        "     130.0, 150.0, 145.0, 150.0, 170.0, 225.0, 145.0, 188.0, 180.0,\n",
        "     197.0, 218.0, 300.0, 260.0, 265.0, 250.0, 250.0, 300.0, 320.0,\n",
        "     514.0, 556.0, 840.0, 685.0, 700.0, 700.0, 690.0, 900.0, 650.0,\n",
        "     820.0, 850.0, 900.0, 1015.0, 820.0, 1100.0, 1000.0, 1100.0,\n",
        "     1000.0, 1000.0]\n",
        "     )\n",
        "train_input, test_input, train_target, test_target = train_test_split(perch_full, perch_weight, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "EclugdXmSs-L"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "\n",
        "poly = PolynomialFeatures(include_bias=False)\n",
        "poly.fit(train_input)\n",
        "train_poly = poly.transform(train_input)\n",
        "test_poly = poly.transform(test_input)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E9ygy-8WOvIP",
        "outputId": "2ce2ada3-7d06-45ef-a9e3-0b0315ccbf9c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9903183436982125\n",
            "0.9714559911594111\n"
          ]
        }
      ],
      "source": [
        "lr = LinearRegression()\n",
        "lr.fit(train_poly, train_target)\n",
        "print(lr.score(train_poly, train_target))\n",
        "print(lr.score(test_poly, test_target))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "2fDt5mrReMwU"
      },
      "outputs": [],
      "source": [
        "poly = PolynomialFeatures(degree=5, include_bias=False) # 특성의 수를 너무 늘린 경우\n",
        "\n",
        "poly.fit(train_input)\n",
        "train_poly = poly.transform(train_input)\n",
        "test_poly = poly.transform(test_input)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UffVFVTGP8xj",
        "outputId": "d72e87b2-9985-45b4-ae59-45567ab099f4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9999999999996433\n",
            "-144.40579436844948\n"
          ]
        }
      ],
      "source": [
        "lr.fit(train_poly, train_target)\n",
        "print(lr.score(train_poly, train_target))\n",
        "print(lr.score(test_poly, test_target))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K2YMPSelQBpO"
      },
      "source": [
        "### 규제"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "hCC7wKy3QQrE"
      },
      "outputs": [],
      "source": [
        "# 표준 점수 변환\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "ss = StandardScaler()\n",
        "ss.fit(train_poly)\n",
        "train_scaled = ss.transform(train_poly)\n",
        "test_scaled = ss.transform(test_poly)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LdNuDNQGQipv",
        "outputId": "87fe27b8-e314-4a7e-85d3-c6cb8373c746"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9896101671037343\n",
            "0.9790693977615387\n"
          ]
        }
      ],
      "source": [
        "# 릿지\n",
        "from sklearn.linear_model import Ridge\n",
        "\n",
        "ridge = Ridge()\n",
        "ridge.fit(train_scaled, train_target)\n",
        "print(ridge.score(train_scaled, train_target))\n",
        "print(ridge.score(test_scaled, test_target))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "9MvIvQOrhfqC"
      },
      "outputs": [],
      "source": [
        "alpha_list = [0.001, 0.01, 0.1, 1, 10, 100]\n",
        "train_score = []\n",
        "test_score = []\n",
        "\n",
        "for alpha in alpha_list:\n",
        "    ridge = Ridge(alpha=alpha)\n",
        "    ridge.fit(train_scaled, train_target)\n",
        "    train_score.append(ridge.score(train_scaled, train_target))\n",
        "    test_score.append(ridge.score(test_scaled, test_target))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "95DjrJxlhiow",
        "outputId": "c29f030d-888e-4a39-b466-fb21b146ea05"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(np.log10(alpha_list), train_score)\n",
        "plt.plot(np.log10(alpha_list), test_score)\n",
        "plt.xlabel('alpha')\n",
        "plt.ylabel('R^2')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5S5vhi-vhjzT",
        "outputId": "0a289e9f-3cd6-45f6-a031-60f97d8a13fe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9903815817570367\n",
            "0.9827976465386928\n"
          ]
        }
      ],
      "source": [
        "ridge = Ridge(alpha=0.1)\n",
        "ridge.fit(train_scaled, train_target)\n",
        "\n",
        "print(ridge.score(train_scaled, train_target))\n",
        "print(ridge.score(test_scaled, test_target))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ymu-jmekh0IK",
        "outputId": "bef43322-0b03-4d6f-ff54-2d30c22d9acd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.989789897208096\n",
            "0.9800593698421883\n"
          ]
        }
      ],
      "source": [
        "# 라쏘\n",
        "from sklearn.linear_model import Lasso\n",
        "\n",
        "lasso = Lasso()\n",
        "lasso.fit(train_scaled, train_target)\n",
        "print(lasso.score(train_scaled, train_target))\n",
        "print(lasso.score(test_scaled, test_target))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uoL2oJ6Ih4Jw",
        "outputId": "63cfd9a2-c47e-4921-b283-b6ee3a769317",
        "scrolled": true
      },
      "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: 1.878e+04, tolerance: 5.183e+02\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: 1.297e+04, tolerance: 5.183e+02\n",
            "  model = cd_fast.enet_coordinate_descent(\n"
          ]
        }
      ],
      "source": [
        "train_score = []\n",
        "test_score = []\n",
        "alpha_list = [0.001, 0.01, 0.1, 1, 10, 100]\n",
        "for alpha in alpha_list:\n",
        "    lasso = Lasso(alpha=alpha, max_iter=10000)\n",
        "    lasso.fit(train_scaled, train_target)\n",
        "    train_score.append(lasso.score(train_scaled, train_target))\n",
        "    test_score.append(lasso.score(test_scaled, test_target))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "7rkH8Dvzh9UI",
        "outputId": "44112345-4577-4029-ca95-c630f90b9353"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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Eiy++GIBf/vKXPPPMM7z00ktccMEFfPbZZyxatIiYmBh+/OMfD/QliviPuwWO77BaeA6shxM7rdaLVs5QSL/c6tbKuAaSz9O08p44gyB6pHXrjum2utV6DEpFVtdbXal1K/qy+/cNi+86KAW5OnZVtU4Z73PAGXNmF1VYbK+/ooDgcFiDoXe8aM0G8wYgsFaF3pJfRk5eCQtnpdtXo5wzw+PxeOz68BkzZjB9+nR++9vfAmCaJmlpadx3330sW7bsjONTU1N56KGHWLx4se+52267jbCwMF555RUAbrrpJlJSUviv//qvLo/pSVVVFTExMVRWVhIdPcyadmVoqyjwtvB8CIdyzlzlNmliWyvP2MutQcdiH9O0upg6BKV299vPiDObz/5zQqPPDDdxYxVwzkXeB/Dq7RCVCkv3+P7xsPdkFTc8uYmwYCe7Hr4WV7AGzA8mffn9bVsLUFNTEzt27GD58uW+5xwOB/PmzWPLli2dntPY2IjL5erwXFhYGJs3b/Y9njVrFs8//zx5eXlkZWXx+eefs3nzZlavXt1lLY2NjTQ2NvoeV1VVne1lifSvplpr/E5rK0/Z/o6vu2IhY64VejLmQsxoW8qULjgcEJlk3UZc2PVxHo815bqm8MxxSa23lnqIOX01Y+9NAaf/jZsNwRHWoPkTu2DUJQBMHBFFclQoxdWNfHa4nCsyE20uVM6WbQGotLQUt9tNSkrHqYQpKSns27ev03Pmz5/P6tWrmT17NhkZGaxfv541a9bgdrt9xyxbtoyqqiomTpyI0+nE7Xbz2GOPceedd3ZZy6pVq/iXf/mX/rkwkXPh8UDRV21bTRRstdbDaWU4YfT0ttlaqRdryvZwYBjWmKyIBEi5wO5qBKyZiBOugb1/sVaF9gYgwzCYnZXE/9txjJy8YgWgIcz2QdB98eSTT3L33XczceJEDMMgIyODRYsW8fvf/953zJ/+9Cf++Mc/8uqrr3LBBRewe/du7r//flJTU1m4cGGn77t8+XKWLl3qe1xVVUVaWg+zSkT6S00J5G/wztj60OoSaS9mjNWllXGN9a9S/WtfZGBkL/AGoHc77g7vC0AlPHSjjfXJObEtACUmJuJ0Oikq6viXfVFRESNGjOj0nKSkJN566y0aGhooKysjNTWVZcuWMX78eN8xP/nJT1i2bBnf+c53ALjwwgs5cuQIq1at6jIAhYaGEhp6FrMvRM5GdxuKgrWpaPqV3inqV0PCBA1eFrFD1nyr1bXoK2tLGO/6WFdMSMRhQF5RDScq6kmN1Vi7oci2ABQSEsLUqVNZv349t9xyC2ANgl6/fj1Llizp9lyXy8WoUaNobm7mjTfe4Nvf/rbvtbq6OhynLXfvdDoxTfP0txEZOD1tKDriwrbZWmMuO7vp0CLSv8LjYcxMOLIZctfCZfcAEBcRwuS0WHYVVLAxr4TvXDrG5kLlbNjaBbZ06VIWLlzItGnTuPTSS3niiSeora1l0aJFANx1112MGjWKVatWAbBt2zaOHz/OlClTOH78OI888gimafLggw/63vPmm2/mscceY8yYMVxwwQXs2rWL1atX83d/93e2XKMEKN+Got6FCHuzoaiIDD7ZN3gD0Du+AARWN9iuggo27lcAGqpsDUB33HEHJSUlPPzwwxQWFjJlyhTWrl3rGxhdUFDQoTWnoaGBFStWkJ+fT2RkJAsWLODll18mNjbWd8xTTz3Fz3/+c/7hH/6B4uJiUlNT+dGPfsTDDz880JcngcQ04eRu72yts9xQVEQGn4kL4IOHrNmY9eXWth9YAeiJv+5n0/5SWtwmQU79/zzU2LoO0GCldYCkV3qzoWhr4Em/YnBt+Cgivff0ZVCyF/7mP+Gi2wFwmx6m/us6Kuqa+X/3zGRaerzNRQoMkXWARIacobahqIj0j+wbrACU+44vADkdBldmJvG/n58gJ69EAWgIUgAS6UqgbCgqIt2beCNsXg37/2rN4gwKAWB2ZqIvAP3Tddk2Fyl9pQAk0l6vNhS92rqNnxtYG4qKBKrUS6zNamuKrFmcE64BrHFAAF8cq6S0ppHESM3eHEoUgCSwtd9Q9OCH1v3TNxQdO6utlUcbiooEHocDsq6HnS9ZiyJ6A1BytIvzRkaz92QVm/eXcsvFo2wuVPpCAUgCT0VB25o82lBURHpj4o3eAPQeLPi17x9Cc7KS2Huyipy8EgWgIUYBSIa/3mwoOv6qtpWXtaGoiJxu3Gxrlfaq49bq7alTACsAPZtzkI15JZimB4dDLcRDhQKQDC+tu2qXH4LDm7vZUHSat5VHG4qKSC8Eh1n/QNr3ttUN5g1AU8fGERHipKy2iT0nq5g0KsbeOqXXFIBk6Gmut7qxyo9YKyyXH4aK1vtHoKn6zHO0oaiInKuJN7YFoLk/AyAkyMGsCYms21NETl6JAtAQogAkg4/phuqTbQGnfbgpPww1hT2/R+QIGHlRWyuPNhQVkXOVOR8MBxR+af0jLNbaAmNOVpIVgHJLWDx3gs1FSm8pAIk96su7DjiVRzt2WXUmJArixlqLDcalQ2zr/bHWX0oauCwi/S0iAdIusxZEzV0LM34ItE2H31FQTlVDM9EurQc2FCgAiX+0NELFUW/AOdwx4FQcOXPm1ekcQdZg5NPDTVw6xKZbuzSrRUdEBlr2Dd4A9I4vAKXFhzM+MYL80lo+OVDK9ZNG2lyk9IYCkJwd07QWBeusBafiCFSdAHrYZi4iqYuAMxaiR4FTfzxFZJCZeCOs+7k1yaK+wjeecHZWEvmlteTklSgADRH6DSNda6jqOuCUHwF3Y/fnB4d3HXDixkJIhL+vQESkfyVkQGI2lObCgb/Chd8CYE52En/45DA5uSV4PB4MtVAPegpAgaylyRpv02nAOWyN0+mO4bC6qToEnHFtjyMS1U0lIsNP9g1WAMp91xeALhuXQEiQgxOVDRworiEzJcrmIqUnCkDDmccDNcVdB5yq4x23fehMeEIXLTjpVvjR5p8iEmgm3ggfPwH71/k2Rw0LcTJjXDyb9peSk1eiADQEKAANdY01bV1SHbqrDlvTNJvruj8/yNV1wIkbC6H6n1hEpINR06wxjLUlcORjyJgLWLPBWgPQ/7lyvM1FSk8UgAY7d4u1I3lXU8brSnt4A8MaUNxVwIlMUTeViEhftG6OuutlqxvMG4Cuyk7iX9/Zy7ZDp6hvchMWohXmBzMFILt5PFBX5g00hzpZE+cYeNzdv4crtouAk251UwWF+vkiREQCzMQbvQHoPbjh38EwyEiKZFRsGMcr6tl6qIy52cl2VyndUAAaSKX7rV3ITx+P01TT/XnOkLaZU2cs+jdW2zqIiAy08VdBUJg1kaTwSxh5EYZhMDsrkf+7/Sg5uSUKQIOcAtBAOvYZvPdg569FpXYecOLSrW0dHI4BLFRERLrVujlq7jtWN9jIiwBrHND/3X6UjXklNhcoPVEAGkgp58N5N3fsoopLh5g0CHbZXJyIiPTJxAVtAeiqZQDMmpCI02GQX1pLQVkdYxLCbS5SuqIANJBGToY7XrG7ChER6Q+Z8wEDTn5ujdeMGU20K5ipY+LYfvgUOftL+H7CWLurlC6oX0VERORsRCZB2gzrfu57vqfnZFubo6obbHBTABIRETlbExdYP3Pf9T3Vujv8JwdKaWrpYbFZsY0CkIiIyNnK9gagQ5ugoRKA80dGkxgZQm2Tmx1HethSSGyjACQiInK2EjMhIRPMZmtzVMDhMJidabUC5agbbNBSABIRETkXvm6wtnFAs7MUgAY7BSAREZFz0doNtv8DcDcDcGVmIoYBe09WUVTVYGNx0hUFIBERkXMxejqEJ1pjgI58AkBCZCgXjooBNBtssFIAEhERORcOJ2Rfb93vZDaYusEGJwUgERGRc9XaDbbvXWuTa9oC0OYDpbhNj12VSRcUgERERM7V+LkQ5ILKAij6GoApabFEuYKoqGvmi2MV9tYnZ1AAEhEROVch4VYIAl83WJDTwZWZiYC6wQYjBSAREZH+0Dodft87vqc0DmjwUgASERHpD1nXY22OuhsqjwNt6wF9frSC8tom+2qTMygAiYiI9IfIZGtKPECetSjiyJgwslIiMT3WYGgZPBSARERE+svEdrPBvNQNNjgpAImIiPSX7Butn4c2QkMVAHOykgErAHk8mg4/WCgAiYiI9JfETIjPsDZHPbgegGnpcYQFOympbmTvyWqbC5RWCkAiIiL9xTDO2BzVFexkZkYCABv3qxtssFAAEhER6U+tq0Lnve/bHNU3DihXAWiwUAASERHpT2kzIDwBGiqgYAvQFoA+O3KKmsYWG4uTVgpAIiIi/cnh9K4JhK8bLD0xgrEJ4TS7PWw5WGZjcdJKAUhERKS/Zd9g/dz3jm9z1NmZrdPhi+2qStpRABIREelvGVdbm6NWHIHiPUBbN9hHuZoOPxgoAImIiPS3kAgYf5V137s56syMBIKdBsfK6zlUWmtfbQIoAImIiPiHrxvMCkARoUFMT48HtCr0YKAAJCIi4g9ZNwAGnNgJVSeBtm6wjQpAtlMAEhER8YeoFBg9zbrv3Rx1TrYVgLbkl9HQ7LarMkEBSERExH9O6wbLTokiJTqUhmaTTw+fsrEwUQASERHxF9/mqDnQWINhGFoVepBQABIREfGXpGyIGwfuJt/mqO13hxf7KACJiIj4i2HARG8rkLcb7IoJiTgM2F9cw/GKehuLC2wKQCIiIv7Uujnq/vfB3UJMeDBT0mIBzQazkwKQiIiIP6XNgLA4qC+Ho1uBdt1gGgdkGwUgERERf3IGtW2O6u0Ga50O//GBUprdpl2VBTQFIBEREX9r7QbLfRc8Hi4cFUNceDDVjS3sPlpha2mBSgFIRETE3zKuBmcolB+Ckn04HQZXZmo6vJ0UgERERPwtNBLGz7HuezdH9a0HpIHQtlAAEhERGQinrQp9ZVYiAF8er6S0ptGuqgKWApCIiMhAyPIGoOOfQXUhyVEuzh8ZDcCm/WoFGmgKQCIiIgMheiSMmmrdz1sLtM0G0ziggacAJCIiMlBO6wZrHQe0cX8ppumxq6qApAAkIiIyUFo3R83/CBpruGRMHJGhQZyqbeLrE1W2lhZoFIBEREQGSvJ5EJcO7kbI30BIkINZGQkA5OQV21tbgFEAEhERGSiG0bYo4mmrQms6/MBSABIRERlIrQEoby2YbmZ7F0TcWVBBZX2zjYUFFgUgERGRgTRmJrhiof4UHN1GWnw4GUkRuE0Pnxwotbu6gGF7AHr66adJT0/H5XIxY8YMtm/f3uWxzc3NPProo2RkZOByuZg8eTJr164947jjx4/zve99j4SEBMLCwrjwwgv57LPP/HkZIiIiveMMgqz51v197wAwW6tCDzhbA9Drr7/O0qVLWblyJTt37mTy5MnMnz+f4uLOB4KtWLGC5557jqeeeoo9e/Zwzz33cOutt7Jr1y7fMeXl5Vx++eUEBwfz3nvvsWfPHh5//HHi4uIG6rJERES6d9rmqO23xfB4NB1+IBgeG7/pGTNmMH36dH77298CYJomaWlp3HfffSxbtuyM41NTU3nooYdYvHix77nbbruNsLAwXnnlFQCWLVvGxx9/zKZNm866rqqqKmJiYqisrCQ6Ovqs30dERKRTjdXw7+PB3QSLt9MQO4HJ//IBjS0mHzwwm6yUKLsrHJL68vvbthagpqYmduzYwbx589qKcTiYN28eW7Zs6fScxsZGXC5Xh+fCwsLYvHmz7/Ff/vIXpk2bxu23305ycjIXX3wxL7zwQre1NDY2UlVV1eEmIiLiN6FRMG62dX/fO7iCncwYb02H36husAFhWwAqLS3F7XaTkpLS4fmUlBQKCws7PWf+/PmsXr2a/fv3Y5om69atY82aNZw8edJ3TH5+Ps888wyZmZm8//773Hvvvfz4xz/mpZde6rKWVatWERMT47ulpaX1z0WKiIh0xdcN9h6g3eEHmu2DoPviySefJDMzk4kTJxISEsKSJUtYtGgRDkfbZZimySWXXMIvfvELLr74Yn74wx9y99138+yzz3b5vsuXL6eystJ3O3r06EBcjoiIBLLWbTGOfQo1xb4AtC3/FHVNLTYWFhhsC0CJiYk4nU6Kioo6PF9UVMSIESM6PScpKYm33nqL2tpajhw5wr59+4iMjGT8+PG+Y0aOHMn555/f4bzzzjuPgoKCLmsJDQ0lOjq6w01ERMSvolMh9WLAA7nvkZEUwajYMJrcJtvyT9ld3bBnWwAKCQlh6tSprF+/3vecaZqsX7+emTNndnuuy+Vi1KhRtLS08MYbb/DNb37T99rll19Obm5uh+Pz8vIYO3Zs/16AiIjIuWrdGyz3PQzD0KrQA8jWLrClS5fywgsv8NJLL7F3717uvfdeamtrWbRoEQB33XUXy5cv9x2/bds21qxZQ35+Pps2beL666/HNE0efPBB3zEPPPAAW7du5Re/+AUHDhzg1Vdf5fnnn+8wc0xERGRQaO0Gy98ATbUaBzSAguz88DvuuIOSkhIefvhhCgsLmTJlCmvXrvUNjC4oKOgwvqehoYEVK1aQn59PZGQkCxYs4OWXXyY2NtZ3zPTp03nzzTdZvnw5jz76KOPGjeOJJ57gzjvvHOjLExER6V7KBRA7BioKIP8jZmVcR5DD4FBpLUfKahmbEGF3hcOWresADVZaB0hERAbMe8tg2zMw5Xtwy9N8+7ktbD90iv/vmxfw/Znpdlc3pAyJdYBERESEtm4w7+ao6gYbGApAIiIidho7C1wxUFcKxz71BaBPDpbR1GLaXNzwpQAkIiJiJ2cwZF5n3d/3DuePjCYxMpS6JjefHdF0eH9RABIREbFbu81RHQ6D2VmJgLrB/EkBSERExG4T5oEjGMoOQOn+tnFAuQpA/qIAJCIiYjdXNIy70rq/7x2uzEzCMGBfYTVFVQ321jZMKQCJiIgMBu26weIjQrhoVAygbjB/UQASEREZDFoD0NHtUFOi6fB+pgAkIiIyGMSMgpGTAQ/krfXtC7Z5fyluU2sW9zcFIBERkcGi3eaok0fHEu0KorK+mc+PVdha1nCkACQiIjJYtK4KffBDgtwNXJmp2WD+ogAkIiIyWIy4EGLGQEs95H+kcUB+pAAkIiIyWBhGWytQ7rvM9gagz49VUF7bZGNhw48CkIiIyGDSbnPUEVHBTBwRhccDmw6U2lvXMKMAJCIiMpikXwGhMVBbAsc+06rQfqIAJCIiMpg4gyHzWut+u26wnLwSTE2H7zcKQCIiIoNNu3FA09LjCAt2UlrTyN7CKnvrGkYUgERERAabzGutzVFL8witOMSsjAQANuZpHFB/UQASEREZbFwx1lgggNx3fatC5+QV21jU8KIAJCIiMhi12xy1dSD0Z4fLqWlssbGo4UMBSEREZDBqHQd0dBtjXfWkJ4TTYnr4RNPh+4UCkIiIyGAUm2atDO0xIe99rQrdz/ocgD7//HP+9V//ld/97neUlnZMoVVVVfzd3/1dvxUnIiIS0Hybo7YfB1SCx6Pp8OeqTwHogw8+4NJLL+W1117jl7/8JRMnTmTDhg2+1+vr63nppZf6vUgREZGANNE7Dujgh1yWFk6I08Gx8nryS2vtrWsY6FMAeuSRR/jnf/5nvvrqKw4fPsyDDz7IN77xDdauXeuv+kRERALXiIsgejQ01xF+7GOmj4sDtCp0f+hTAPr66699XVyGYfDggw/y3HPP8a1vfYu3337bLwWKiIgErA6bo77jGwe0cb8C0LnqUwAKDQ2loqKiw3N/+7d/y3/+539yxx138Oabb/ZnbSIiItLaDZa7ljmZiQBszS+jodltY1FDX1BfDp4yZQobNmxg6tSpHZ7/zne+g8fjYeHChf1anIiISMAbewWERkNtMVktuYyIdlFY1cD2Q6d8+4RJ3/WpBejee+/l+PHjnb723e9+lz/84Q/Mnj27XwoTERERICgEJswDwMh9T9Ph+4nh0Vy6M1RVVRETE0NlZSXR0dF2lyMiIoHui/+BNf8Hkiby7uy3+Ic/7mRCciR/XTrH7soGlb78/tZCiCIiIoNd5rXgCIKSfVyZUIXTYXCguIZj5XV2VzZknVUAWrNmTX/XISIiIl0Ji4WxlwMQdXgdF6fFAtod/lz0OQA9//zz3Hffff6oRURERLrSbnPU2VnaHf5c9SkAPfbYY/zsZz/j3Xff9Vc9IiIi0pnW6fAFW7h6jBOAjw+U0ew2bSxq6Op1ALr//vv593//d9555x0mT57sz5pERETkdLFjIMXaHPX8mq3ER4RQ09jCroIKuysbknodgP7jP/6Dxx9/nBkzZvizHhEREemKd1VoR967XOldFFHdYGen1wHotttuY+XKleTn5/uzHhEREelKazfYgQ+Zm2FN89Z6QGen1wHoT3/6EzfddBPXXHNNl4shioiIiB+NnAJRqdBcy9yQfQB8dbyKkupGe+sagnodgAzD4LnnnuO73/0uV199tT9rEhERkc602xw1puADJo2yWoE2aXPUPuvzNPhf/OIX3Hvvvf6oRURERHrSYXPUBEDdYGfjrBZCvP/++7t8rb6+/mxrERERkZ6kXwkhUVBTyA3xhQBszCvBbWpnq77ot60wGhsbefzxxxk3blx/vaWIiIicLigUJlwDwHmVm4gMDaK8rpmvjlfaXNjQ0qcA1NjYyPLly5k2bRqzZs3irbfeAuDFF19k3LhxPPHEEzzwwAP+qFNERERaTbwRAOf+tVw+weoG26husD7pUwB6+OGHeeaZZ0hPT+fw4cPcfvvt/PCHP+Q3v/kNq1ev5vDhw/z0pz/1V60iIiICMGEeGE4o3sOC0dYMMI0D6pugvhz8P//zP/z3f/833/jGN/jqq6+46KKLaGlp4fPPP8cwDH/VKCIiIu2Fx8PYWXB4E3PM7UAWOwvKqaxrJiY82O7qhoQ+tQAdO3aMqVOnAjBp0iRCQ0N54IEHFH5EREQGmrcbLPboeiYkR2J64OOD2h2+t/oUgNxuNyEhIb7HQUFBREZG9ntRIiIi0gPvekAc+YT5463fzTm56gbrrT51gXk8Hn7wgx8QGhoKQENDA/fccw8REREdjluzZk3/VSgiIiJnikuH5Aug+GtuDvuKp0klJ68Ej8ejnple6FMAWrhwYYfH3/ve9/q1GBEREemDiQug+GsyyzfiCv5bCqsayCuqIXtElN2VDXp9CkAvvviiv+oQERGRvsq+ATb+Cmf+h1ye/iPW768kJ69YAagX+m0hRBERERlgIy+GqJHQVMO3Ew4DsDFPA6F7QwFIRERkqHI4IOt6AC5r3grA9kOnqGtqsbOqIUEBSEREZCjzToePLvgrabGhNLlNtuaX2VzU4KcAJCIiMpSNmw0hkRjVJ/luWjmg6fC9oQAkIiIylAWFQsbVAMwP2gFoW4zeUAASEREZ6rzdYOmlOQQ5DA6X1XG4tNbmogY3BSAREZGhLvM6MJw4S/Zwg3dz1I371QrUHQUgERGRoS48HsbMBOCO6C8BjQPqiQKQiIjIcDBxAQBT6rYAsCW/jMYWt50VDWoKQCIiIsOBd3PUiJPbGB/ZTF2Tmx2Hy20uavBSABIRERkO4sdD0nkYHjd/n7If0Gyw7igAiYiIDBfebrCr+AxQAOqOApCIiMhwkW0FoNSSzYQazewrrKawssHmogYnBSAREZHhIvUSiByB0VTDd5MLANioVqBOKQCJiIgMFw4HZFubo94S9jmgbrCuKACJiIgMJ95usPOrPwY8bNpfQovbtLemQUgBSEREZDgZNweCIwipPckM1zGqGlr4/Fil3VUNOgpAIiIiw0mwCyZYm6MujP8aUDdYZxSAREREhhtvN9jMlm2AAlBnBkUAevrpp0lPT8flcjFjxgy2b9/e5bHNzc08+uijZGRk4HK5mDx5MmvXru3y+H/7t3/DMAzuv/9+P1QuIiIyCGXOB8NBXFUuoyjhi2MVnKptsruqQcX2APT666+zdOlSVq5cyc6dO5k8eTLz58+nuLi40+NXrFjBc889x1NPPcWePXu45557uPXWW9m1a9cZx3766ac899xzXHTRRf6+DBERkcEjIgHSLgPgzriv8Xhgk3aH78D2ALR69WruvvtuFi1axPnnn8+zzz5LeHg4v//97zs9/uWXX+ZnP/sZCxYsYPz48dx7770sWLCAxx9/vMNxNTU13HnnnbzwwgvExcV1W0NjYyNVVVUdbiIiIkOad1XoG4J3AuoGO52tAaipqYkdO3Ywb94833MOh4N58+axZcuWTs9pbGzE5XJ1eC4sLIzNmzd3eG7x4sXceOONHd67K6tWrSImJsZ3S0tLO4urERERGUS844DGVu8mmlo25pVimh6bixo8bA1ApaWluN1uUlJSOjyfkpJCYWFhp+fMnz+f1atXs3//fkzTZN26daxZs4aTJ0/6jnnttdfYuXMnq1at6lUdy5cvp7Ky0nc7evTo2V+UiIjIYJCQAYnZODwtXBfyJaU1jew5qR6OVrZ3gfXVk08+SWZmJhMnTiQkJIQlS5awaNEiHA7rUo4ePco//uM/8sc//vGMlqKuhIaGEh0d3eEmIiIy5Hm7wb4d9QWgbrD2bA1AiYmJOJ1OioqKOjxfVFTEiBEjOj0nKSmJt956i9raWo4cOcK+ffuIjIxk/PjxAOzYsYPi4mIuueQSgoKCCAoKIicnh//4j/8gKCgIt9vt9+sSEREZFLJvBGBK42cE06J9wdqxNQCFhIQwdepU1q9f73vONE3Wr1/PzJkzuz3X5XIxatQoWlpaeOONN/jmN78JwDXXXMOXX37J7t27fbdp06Zx5513snv3bpxOp1+vSUREZNAYNRUikglpqWGGYy87jpRT3dBsd1WDQpDdBSxdupSFCxcybdo0Lr30Up544glqa2tZtGgRAHfddRejRo3yjefZtm0bx48fZ8qUKRw/fpxHHnkE0zR58MEHAYiKimLSpEkdPiMiIoKEhIQznhcRERnWWjdH3fnf/E3EF2yuvpBPDpYx/4LOe1kCie0B6I477qCkpISHH36YwsJCpkyZwtq1a30DowsKCnzjewAaGhpYsWIF+fn5REZGsmDBAl5++WViY2NtugIREZFBLHsB7PxvruEz4G/JyStRAAIMj8ejOXGnqaqqIiYmhsrKSg2IFhGRoa25Hv59PDTXcWPjL6iIOY/NP52LYRh2V9bv+vL7e8jNAhMREZE+CA6DDGtz1PlBOzleUc/Bklqbi7KfApCIiMhwl30DAN9w7QY0HR4UgERERIa/rOvBcJDefIBUShWAUAASEREZ/iISIW0GANc4d7Itv4yG5sBeF08BSEREJBB4u8FuCtlFY4vJtkOnbC7IXgpAIiIigcC7KvRUviaKOnJyA7sbTAFIREQkECROgMQsgjwtzHF8Tk5esd0V2UoBSEREJFB4u8Guc+7gYEktR0/V2VyQfRSAREREAoW3G+yaoM8JooWN+wO3G0wBSEREJFCMngbhiUR4arnUsS+gxwEpAImIiAQKh9PaHBW41rGDTw6W0ew2bS7KHgpAIiIigcTbDTY/aCc1jc3sPFJuc0H2UAASEREJJOOvgqAwUinhPKMgYFeFVgASEREJJCHhkDEXsLrBFIBEREQkMGQvAGCecwdfn6iiuLrB5oIGngKQiIhIoMmaDxhc5DjECMrYlFdqd0UDTgFIREQk0EQmQ9qlAMxz7gzIbjAFIBERkUDk7Qa71rGDTftLcJsemwsaWApAIiIigcgbgGY5v6a5rpIvj1faXNDAUgASEREJRElZkDCBYNzMdnzBxgDrBlMAEhERCVTezVGvdQbedHgFIBERkUDlXRX6ascuviwoobKu2eaCBo4CkIiISKBKuxTCE4gx6phq5LL5QOBMh1cAEhERCVQOJ2S1bY6ak1dsc0EDRwFIREQkkLWbDp+TW4zHExjT4RWAREREAlnGXDxBLtIcJcTWHCC3qNruigaEApCIiEggC4nAGH8V0NoKFBizwRSAREREAl27zVE37lcAEhERkUCQdT0eDKY48jly6CC1jS12V+R3CkAiIiKBLioFRk8DYA6fsTW/zOaC/E8BSERERDBau8EcgbEqtAKQiIiItG2O6via7bkFNhfjfwpAIiIiAknZmHHjCTVaGFexlcOltXZX5FcKQCIiIgKGgWNi22yw4d4NpgAkIiIiFm832NWO3WzKLbS5GP9SABIRERFL2gxaXHHEGTU05X9CY4vb7or8RgFIRERELM4gnNnW5qizPdv57HC5zQX5jwKQiIiI+BinbY46XCkAiYiISJuMq3E7QhjrKObw3h12V+M3CkAiIiLSJjQSM302ABPKN3Kyst7mgvxDAUhEREQ6CD7/JgCuc+5g4zCdDq8AJCIiIh1l3wDAFMdBdu3ZZ3Mx/qEAJCIiIh1FjaA2cTIAYYfW0eI2bS6o/ykAiYiIyBnCLvwGAFe6t7P7aIW9xfiBApCIiIicwXHejQBc7viaLXuP2FxN/1MAEhERkTMlTaQmfDShRjM1ez6wu5p+pwAkIiIiZzIMHBOtVqDMis2U1TTaXFD/UgASERGRToVfZI0Dutqxk4/zhtfmqApAIiIi0rm0y6gPiibeqKHg84/srqZfKQCJiIhI55xB1Iy5BoCYgnWYpsfmgvqPApCIiIh0KfbibwJwhXs7e05U2lxN/1EAEhERkS4FZ82jmWDGOYr4Yvd2u8vpNwpAIiIi0rXQKIoTZwDgyX3X5mL6jwKQiIiIdCvsQmtz1PMrN1PV0GxzNf1DAUhERES6FX/xLQBMNg7w2VfDY3NUBSARERHpXvRIjoefh8PwUL7rf+2upl8oAImIiEiPGjPmA5Bycj0ez9CfDq8AJCIiIj1KnfEtAKa5Pyf/RLHN1Zw7BSARERHpkWvUJEqcKbiMZvK3vW13OedMAUhERER6ZhgUpVqrQoceWGtzMedOAUhERER6Je4Sa1XoSbVbaGhssrmac6MAJCIiIr2SeuHVVBFBvFHN3k//anc550QBSERERHrFCArhYOwsAOq/HNrT4RWAREREpPcm3ghAWvFH9tZxjhSAREREpNcyZn6TJo+TNM8JTh743O5yzpoCkIiIiPRadEw8e0KnAHBy+xp7izkHCkAiIiLSJxVjrwUg+sg6mys5ewpAIiIi0icjpt0KwPiGPTRVFNpczdkZFAHo6aefJj09HZfLxYwZM9i+fXuXxzY3N/Poo4+SkZGBy+Vi8uTJrF3bcUGmVatWMX36dKKiokhOTuaWW24hNzfX35chIiISELIys9nDeByGh4Jtb9pdzlmxPQC9/vrrLF26lJUrV7Jz504mT57M/PnzKS7ufJ+RFStW8Nxzz/HUU0+xZ88e7rnnHm699VZ27drlOyYnJ4fFixezdetW1q1bR3NzM9dddx21tbUDdVkiIiLDlsNhUJA4BwDPvndsrubsGB6bt3SdMWMG06dP57e//S0ApmmSlpbGfffdx7Jly844PjU1lYceeojFixf7nrvtttsICwvjlVde6fQzSkpKSE5OJicnh9mzZ/dYU1VVFTExMVRWVhIdHX2WVyYiIjJ8bchZz9wNf0MDobh+dhhCwu0uqU+/v21tAWpqamLHjh3MmzfP95zD4WDevHls2bKl03MaGxtxuVwdngsLC2Pz5s1dfk5lZSUA8fHxXb5nVVVVh5uIiIh07aJLLueYJxEXjVR8/YHd5fSZrQGotLQUt9tNSkpKh+dTUlIoLOx8UNX8+fNZvXo1+/fvxzRN1q1bx5o1azh58mSnx5umyf3338/ll1/OpEmTOj1m1apVxMTE+G5paWnndmEiIiLDXEKUi11hMwEo3/lnm6vpO9vHAPXVk08+SWZmJhMnTiQkJIQlS5awaNEiHI7OL2Xx4sV89dVXvPbaa12+5/Lly6msrPTdjh496q/yRUREho2G8fMBSDzxIZhum6vpG1sDUGJiIk6nk6Kiog7PFxUVMWLEiE7PSUpK4q233qK2tpYjR46wb98+IiMjGT9+/BnHLlmyhLfffpsNGzYwevToLusIDQ0lOjq6w01ERES6N37adVR5wolyV+A++qnd5fSJrQEoJCSEqVOnsn79et9zpmmyfv16Zs6c2e25LpeLUaNG0dLSwhtvvME3v/lN32sej4clS5bw5ptv8uGHHzJu3Di/XYOIiEigmjw2ic3GxQCUfja0VoW2vQts6dKlvPDCC7z00kvs3buXe++9l9raWhYtWgTAXXfdxfLly33Hb9u2jTVr1pCfn8+mTZu4/vrrMU2TBx980HfM4sWLeeWVV3j11VeJioqisLCQwsJC6uvrB/z6REREhqsgp4OTI64GIPjA+zZX0zdBdhdwxx13UFJSwsMPP0xhYSFTpkxh7dq1voHRBQUFHcb3NDQ0sGLFCvLz84mMjGTBggW8/PLLxMbG+o555plnALjqqqs6fNaLL77ID37wA39fkoiISMCIu+gGmk7+gvj6w1B6ABIn2F1Sr9i+DtBgpHWAREREeudkZT37f30ts51fUj9nJWFzl9pWy5BZB0hERESGtpExYXweMQuAuq/etrma3lMAEhERkXOTdT0AcWW7oLbU5mJ6RwFIREREzsnFF17EV2Y6Dkw8ue/ZXU6vKACJiIjIOZmWHscGpgFQ88X/2lxN7ygAiYiIyDlxBTspG3WNdb8gB5oH/7IzCkAiIiJyzsZNmskxTyLBZgPk59hdTo8UgEREROSczclO5q/uSwBo3jP4Z4MpAImIiMg5S0+M4MtIazq8mfsemKbNFXVPAUhERET6RXT2VVR5wghtKIXjO+wup1sKQCIiItIvLs9OJcecDIBn3zs2V9M9BSARERHpFzMzEvjQY02HH+zjgBSAREREpF9EhAZRnTaXZo+TkPL9UHbQ7pK6pAAkIiIi/Wb6xHFsMydaD3LftbeYbigAiYiISL+Zk53EOtPqBjP3Dt5xQApAIiIi0m+yU6LYHX4ZAMaxbVBbZnNFnVMAEhERkX5jGAbZ2RewxxyL4TFh//t2l9QpBSARERHpV3OykllnTrUeDNJxQApAIiIi0q+umJDIX70ByDywHpobbK7oTApAIiIi0q9iwoMJHjWFE554HM11cGjwbY6qACQiIiL9bk52Cn91D95uMAUgERER6XdzspP4q2ntDu8ZhJujKgCJiIhIv7twVAz7QidT7QnDqCmCEzvtLqkDBSARERHpd06HwWVZbZujDrZuMAUgERER8Ys5WUmsc1vdYOxTABIREZEAcGVWIhvMKbR4HFCyF07l212SjwKQiIiI+EVylIu01FS2medZT+S+Z29B7SgAiYiIiN/MzmqbDTaYusEUgERERMRv5mQl+bbF8BRsgbpTNldkUQASERERv7lkTBwVIansNdMwPG7Y/4HdJQEKQCIiIuJHIUEOZmUktG2Ouu8dewvyUgASERERv5qTncQ69zTrwSDZHFUBSERERPxqdmYSX3nSKfTEQXMtHN5kd0kKQCIiIuJfafHhjE+K4q++RRHt7wZTABIRERG/m5OVzDrT2w2Wt9b2zVEVgERERMTvZmclssU8n1pcUH0STu6ytR4FIBEREfG7y8YnYASF8pH7IusJm1eFVgASERERv3MFO5kxPqFtNljBVlvrUQASERGRATEnK4n15iU8kvwk3PUXW2tRABIREZEBMScriWrCefVECvUtHltrUQASERGRAZGRFMGo2DCaWky2HiqztRYFIBERERkQhmEwJzsJgJzcEltrUQASERGRATMnK4kgh0F1Q4utdQTZ+ukiIiISUOZkJbHr4WuJcgXbWocCkIiIiAwYV7ATV7DT7jLUBSYiIiKBRwFIREREAo4CkIiIiAQcBSAREREJOApAIiIiEnAUgERERCTgKACJiIhIwFEAEhERkYCjACQiIiIBRwFIREREAo4CkIiIiAQcBSAREREJOApAIiIiEnC0G3wnPB4PAFVVVTZXIiIiIr3V+nu79fd4dxSAOlFdXQ1AWlqazZWIiIhIX1VXVxMTE9PtMYanNzEpwJimyYkTJ4iKisIwjH5976qqKtLS0jh69CjR0dH9+t7SRt/zwND3PDD0PQ8Mfc8Dx1/ftcfjobq6mtTUVByO7kf5qAWoEw6Hg9GjR/v1M6Kjo/U/2ADQ9zww9D0PDH3PA0Pf88Dxx3fdU8tPKw2CFhERkYCjACQiIiIBRwFogIWGhrJy5UpCQ0PtLmVY0/c8MPQ9Dwx9zwND3/PAGQzftQZBi4iISMBRC5CIiIgEHAUgERERCTgKQCIiIhJwFIBEREQk4CgA2egb3/gGY8aMweVyMXLkSL7//e9z4sQJu8saVg4fPszf//3fM27cOMLCwsjIyGDlypU0NTXZXdqw89hjjzFr1izCw8OJjY21u5xh5emnnyY9PR2Xy8WMGTPYvn273SUNOxs3buTmm28mNTUVwzB466237C5p2Fm1ahXTp08nKiqK5ORkbrnlFnJzc22rRwHIRnPnzuVPf/oTubm5vPHGGxw8eJBvfetbdpc1rOzbtw/TNHnuuef4+uuv+c1vfsOzzz7Lz372M7tLG3aampq4/fbbuffee+0uZVh5/fXXWbp0KStXrmTnzp1MnjyZ+fPnU1xcbHdpw0ptbS2TJ0/m6aeftruUYSsnJ4fFixezdetW1q1bR3NzM9dddx21tbW21KNp8IPIX/7yF2655RYaGxsJDg62u5xh61e/+hXPPPMM+fn5dpcyLP3hD3/g/vvvp6Kiwu5ShoUZM2Ywffp0fvvb3wLWXoVpaWncd999LFu2zObqhifDMHjzzTe55ZZb7C5lWCspKSE5OZmcnBxmz5494J+vFqBB4tSpU/zxj39k1qxZCj9+VllZSXx8vN1liPSoqamJHTt2MG/ePN9zDoeDefPmsWXLFhsrEzl3lZWVALb9fawAZLOf/vSnREREkJCQQEFBAX/+85/tLmlYO3DgAE899RQ/+tGP7C5FpEelpaW43W5SUlI6PJ+SkkJhYaFNVYmcO9M0uf/++7n88suZNGmSLTUoAPWzZcuWYRhGt7d9+/b5jv/JT37Crl27+OCDD3A6ndx1112oV7Jnff2eAY4fP87111/P7bffzt13321T5UPL2XzPIiI9Wbx4MV999RWvvfaabTUE2fbJw9Q//dM/8YMf/KDbY8aPH++7n5iYSGJiIllZWZx33nmkpaWxdetWZs6c6edKh7a+fs8nTpxg7ty5zJo1i+eff97P1Q0fff2epX8lJibidDopKirq8HxRUREjRoywqSqRc7NkyRLefvttNm7cyOjRo22rQwGonyUlJZGUlHRW55qmCUBjY2N/ljQs9eV7Pn78OHPnzmXq1Km8+OKLOBxq+Oytc/nzLOcuJCSEqVOnsn79et+AXNM0Wb9+PUuWLLG3OJE+8ng83Hfffbz55pt89NFHjBs3ztZ6FIBssm3bNj799FOuuOIK4uLiOHjwID//+c/JyMhQ608/On78OFdddRVjx47l17/+NSUlJb7X9C/o/lVQUMCpU6coKCjA7Xaze/duACZMmEBkZKS9xQ1hS5cuZeHChUybNo1LL72UJ554gtraWhYtWmR3acNKTU0NBw4c8D0+dOgQu3fvJj4+njFjxthY2fCxePFiXn31Vf785z8TFRXlG8cWExNDWFjYwBfkEVt88cUXnrlz53ri4+M9oaGhnvT0dM8999zjOXbsmN2lDSsvvviiB+j0Jv1r4cKFnX7PGzZssLu0Ie+pp57yjBkzxhMSEuK59NJLPVu3brW7pGFnw4YNnf75Xbhwod2lDRtd/V384osv2lKP1gESERGRgKPBECIiIhJwFIBEREQk4CgAiYiISMBRABIREZGAowAkIiIiAUcBSERERAKOApCIiIgEHAUgERERCTgKQCIybBw+fBjDMHzbcPTGH/7wB2JjY/1Wk4gMTgpAIiIiEnAUgERERCTgKACJyJCydu1arrjiCmJjY0lISOCmm27i4MGDnR770UcfYRgG77zzDhdddBEul4vLLruMr7766oxj33//fc477zwiIyO5/vrrOXnypO+1Tz/9lGuvvZbExERiYmKYM2cOO3fu9Ns1ioj/KQCJyJBSW1vL0qVL+eyzz1i/fj0Oh4Nbb70V0zS7POcnP/kJjz/+OJ9++ilJSUncfPPNNDc3+16vq6vj17/+NS+//DIbN26koKCAf/7nf/a9Xl1dzcKFC9m8eTNbt24lMzOTBQsWUF1d7ddrFRH/CbK7ABGRvrjttts6PP79739PUlISe/bsITIystNzVq5cybXXXgvASy+9xOjRo3nzzTf59re/DUBzczPPPvssGRkZACxZsoRHH33Ud/7VV1/d4f2ef/55YmNjycnJ4aabbuq3axORgaMWIBEZUvbv3893v/tdxo8fT3R0NOnp6QAUFBR0ec7MmTN99+Pj48nOzmbv3r2+58LDw33hB2DkyJEUFxf7HhcVFXH33XeTmZlJTEwM0dHR1NTUdPuZIjK4qQVIRIaUm2++mbFjx/LCCy+QmpqKaZpMmjSJpqams37P4ODgDo8Nw8Dj8fgeL1y4kLKyMp588knGjh1LaGgoM2fOPKfPFBF7KQCJyJBRVlZGbm4uL7zwAldeeSUAmzdv7vG8rVu3MmbMGADKy8vJy8vjvPPO6/Xnfvzxx/zud79jwYIFABw9epTS0tKzuAIRGSwUgERkyIiLiyMhIYHnn3+ekSNHUlBQwLJly3o879FHHyUhIYGUlBQeeughEhMTueWWW3r9uZmZmbz88stMmzaNqqoqfvKTnxAWFnYOVyIidtMYIBEZMhwOB6+99ho7duxg0qRJPPDAA/zqV7/q8bx/+7d/4x//8R+ZOnUqhYWF/O///i8hISG9/tz/+q//ory8nEsuuYTvf//7/PjHPyY5OflcLkVEbGZ42nd0i4gMIx999BFz586lvLxc212ISAdqARIREZGAowAkIiIiAUddYCIiIhJw1AIkIiIiAUcBSERERAKOApCIiIgEHAUgERERCTgKQCIiIhJwFIBEREQk4CgAiYiISMBRABIREZGA8/8DMUFD9SU41n0AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(np.log10(alpha_list), train_score)\n",
        "plt.plot(np.log10(alpha_list), test_score)\n",
        "plt.xlabel('alpha')\n",
        "plt.ylabel('R^2')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t4uFD9Flh_Dw",
        "outputId": "0bcd140b-3415-462d-956b-4bcb09e46ff9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9888067471131867\n",
            "0.9824470598706695\n"
          ]
        }
      ],
      "source": [
        "lasso = Lasso(alpha=10)\n",
        "lasso.fit(train_scaled, train_target)\n",
        "\n",
        "print(lasso.score(train_scaled, train_target))\n",
        "print(lasso.score(test_scaled, test_target))"
      ]
    }
  ]
}