notebooks/week5_solutions_2.ipynb

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      "source": [
        "# Week 6 - An introduction to machine learning (PART1) - Solutions"
      ]
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        "We will work with the built-in `sklearn.datasets` diabetes dataset.\n",
        "This dataset comprises 442 diabetes patients, each with ten baseline predictors: age, sex, body mass index, average blood pressure, and six blood serum measurements. The response variable is measure of disease progression one year after baseline. (Note that it is not neccessary to transform any of these variables prior to training the model - everything is all ready to go straight into `sklearn`.)"
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      "source": [
        "Details:\n",
        "\n",
        "https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset\n",
        "\n",
        "https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html"
      ]
    },
    {
      "cell_type": "code",
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        "id": "8SA91PY0wgJi"
      },
      "source": [
        "import pandas as pd\n",
        "import plotnine as p9\n",
        "from sklearn import datasets\n",
        "import sklearn.linear_model as linear"
      ],
      "execution_count": 1,
      "outputs": []
    },
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      "source": [
        "diabetes_dataset = datasets.load_diabetes()\n",
        "predictors = pd.DataFrame(data=diabetes_dataset.data, columns=diabetes_dataset.feature_names)\n",
        "response = diabetes_dataset.target\n",
        "\n",
        "predictors.head()"
      ],
      "execution_count": 2,
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              "        age       sex       bmi        bp        s1        s2        s3  \\\n",
              "0  0.038076  0.050680  0.061696  0.021872 -0.044223 -0.034821 -0.043401   \n",
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              "4  0.005383 -0.044642 -0.036385  0.021872  0.003935  0.015596  0.008142   \n",
              "\n",
              "         s4        s5        s6  \n",
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              "1 -0.039493 -0.068330 -0.092204  \n",
              "2 -0.002592  0.002864 -0.025930  \n",
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      "source": [
        "print(diabetes_dataset.DESCR)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ".. _diabetes_dataset:\n",
            "\n",
            "Diabetes dataset\n",
            "----------------\n",
            "\n",
            "Ten baseline variables, age, sex, body mass index, average blood\n",
            "pressure, and six blood serum measurements were obtained for each of n =\n",
            "442 diabetes patients, as well as the response of interest, a\n",
            "quantitative measure of disease progression one year after baseline.\n",
            "\n",
            "**Data Set Characteristics:**\n",
            "\n",
            "  :Number of Instances: 442\n",
            "\n",
            "  :Number of Attributes: First 10 columns are numeric predictive values\n",
            "\n",
            "  :Target: Column 11 is a quantitative measure of disease progression one year after baseline\n",
            "\n",
            "  :Attribute Information:\n",
            "      - age     age in years\n",
            "      - sex\n",
            "      - bmi     body mass index\n",
            "      - bp      average blood pressure\n",
            "      - s1      tc, total serum cholesterol\n",
            "      - s2      ldl, low-density lipoproteins\n",
            "      - s3      hdl, high-density lipoproteins\n",
            "      - s4      tch, total cholesterol / HDL\n",
            "      - s5      ltg, possibly log of serum triglycerides level\n",
            "      - s6      glu, blood sugar level\n",
            "\n",
            "Note: Each of these 10 feature variables have been mean centered and scaled by the standard deviation times `n_samples` (i.e. the sum of squares of each column totals 1).\n",
            "\n",
            "Source URL:\n",
            "https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html\n",
            "\n",
            "For more information see:\n",
            "Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani (2004) \"Least Angle Regression,\" Annals of Statistics (with discussion), 407-499.\n",
            "(https://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf)\n"
          ]
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        "\n",
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              "         response\n",
              "count  442.000000\n",
              "mean   152.133484\n",
              "std     77.093005\n",
              "min     25.000000\n",
              "25%     87.000000\n",
              "50%    140.500000\n",
              "75%    211.500000\n",
              "max    346.000000"
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              "                age           sex           bmi            bp            s1  \\\n",
              "count  4.420000e+02  4.420000e+02  4.420000e+02  4.420000e+02  4.420000e+02   \n",
              "mean  -3.639623e-16  1.309912e-16 -8.013951e-16  1.289818e-16 -9.042540e-17   \n",
              "std    4.761905e-02  4.761905e-02  4.761905e-02  4.761905e-02  4.761905e-02   \n",
              "min   -1.072256e-01 -4.464164e-02 -9.027530e-02 -1.123996e-01 -1.267807e-01   \n",
              "25%   -3.729927e-02 -4.464164e-02 -3.422907e-02 -3.665645e-02 -3.424784e-02   \n",
              "50%    5.383060e-03 -4.464164e-02 -7.283766e-03 -5.670611e-03 -4.320866e-03   \n",
              "75%    3.807591e-02  5.068012e-02  3.124802e-02  3.564384e-02  2.835801e-02   \n",
              "max    1.107267e-01  5.068012e-02  1.705552e-01  1.320442e-01  1.539137e-01   \n",
              "\n",
              "                 s2            s3            s4            s5            s6  \n",
              "count  4.420000e+02  4.420000e+02  4.420000e+02  4.420000e+02  4.420000e+02  \n",
              "mean   1.301121e-16 -4.563971e-16  3.863174e-16 -3.848103e-16 -3.398488e-16  \n",
              "std    4.761905e-02  4.761905e-02  4.761905e-02  4.761905e-02  4.761905e-02  \n",
              "min   -1.156131e-01 -1.023071e-01 -7.639450e-02 -1.260974e-01 -1.377672e-01  \n",
              "25%   -3.035840e-02 -3.511716e-02 -3.949338e-02 -3.324879e-02 -3.317903e-02  \n",
              "50%   -3.819065e-03 -6.584468e-03 -2.592262e-03 -1.947634e-03 -1.077698e-03  \n",
              "75%    2.984439e-02  2.931150e-02  3.430886e-02  3.243323e-02  2.791705e-02  \n",
              "max    1.987880e-01  1.811791e-01  1.852344e-01  1.335990e-01  1.356118e-01  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0bd0dbea-778a-4f44-802a-cbc59a40c118\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>bmi</th>\n",
              "      <th>bp</th>\n",
              "      <th>s1</th>\n",
              "      <th>s2</th>\n",
              "      <th>s3</th>\n",
              "      <th>s4</th>\n",
              "      <th>s5</th>\n",
              "      <th>s6</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4.420000e+02</td>\n",
              "      <td>4.420000e+02</td>\n",
              "      <td>4.420000e+02</td>\n",
              "      <td>4.420000e+02</td>\n",
              "      <td>4.420000e+02</td>\n",
              "      <td>4.420000e+02</td>\n",
              "      <td>4.420000e+02</td>\n",
              "      <td>4.420000e+02</td>\n",
              "      <td>4.420000e+02</td>\n",
              "      <td>4.420000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-3.639623e-16</td>\n",
              "      <td>1.309912e-16</td>\n",
              "      <td>-8.013951e-16</td>\n",
              "      <td>1.289818e-16</td>\n",
              "      <td>-9.042540e-17</td>\n",
              "      <td>1.301121e-16</td>\n",
              "      <td>-4.563971e-16</td>\n",
              "      <td>3.863174e-16</td>\n",
              "      <td>-3.848103e-16</td>\n",
              "      <td>-3.398488e-16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.761905e-02</td>\n",
              "      <td>4.761905e-02</td>\n",
              "      <td>4.761905e-02</td>\n",
              "      <td>4.761905e-02</td>\n",
              "      <td>4.761905e-02</td>\n",
              "      <td>4.761905e-02</td>\n",
              "      <td>4.761905e-02</td>\n",
              "      <td>4.761905e-02</td>\n",
              "      <td>4.761905e-02</td>\n",
              "      <td>4.761905e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-1.072256e-01</td>\n",
              "      <td>-4.464164e-02</td>\n",
              "      <td>-9.027530e-02</td>\n",
              "      <td>-1.123996e-01</td>\n",
              "      <td>-1.267807e-01</td>\n",
              "      <td>-1.156131e-01</td>\n",
              "      <td>-1.023071e-01</td>\n",
              "      <td>-7.639450e-02</td>\n",
              "      <td>-1.260974e-01</td>\n",
              "      <td>-1.377672e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-3.729927e-02</td>\n",
              "      <td>-4.464164e-02</td>\n",
              "      <td>-3.422907e-02</td>\n",
              "      <td>-3.665645e-02</td>\n",
              "      <td>-3.424784e-02</td>\n",
              "      <td>-3.035840e-02</td>\n",
              "      <td>-3.511716e-02</td>\n",
              "      <td>-3.949338e-02</td>\n",
              "      <td>-3.324879e-02</td>\n",
              "      <td>-3.317903e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.383060e-03</td>\n",
              "      <td>-4.464164e-02</td>\n",
              "      <td>-7.283766e-03</td>\n",
              "      <td>-5.670611e-03</td>\n",
              "      <td>-4.320866e-03</td>\n",
              "      <td>-3.819065e-03</td>\n",
              "      <td>-6.584468e-03</td>\n",
              "      <td>-2.592262e-03</td>\n",
              "      <td>-1.947634e-03</td>\n",
              "      <td>-1.077698e-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.807591e-02</td>\n",
              "      <td>5.068012e-02</td>\n",
              "      <td>3.124802e-02</td>\n",
              "      <td>3.564384e-02</td>\n",
              "      <td>2.835801e-02</td>\n",
              "      <td>2.984439e-02</td>\n",
              "      <td>2.931150e-02</td>\n",
              "      <td>3.430886e-02</td>\n",
              "      <td>3.243323e-02</td>\n",
              "      <td>2.791705e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.107267e-01</td>\n",
              "      <td>5.068012e-02</td>\n",
              "      <td>1.705552e-01</td>\n",
              "      <td>1.320442e-01</td>\n",
              "      <td>1.539137e-01</td>\n",
              "      <td>1.987880e-01</td>\n",
              "      <td>1.811791e-01</td>\n",
              "      <td>1.852344e-01</td>\n",
              "      <td>1.335990e-01</td>\n",
              "      <td>1.356118e-01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0bd0dbea-778a-4f44-802a-cbc59a40c118')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "          document.querySelector('#df-0bd0dbea-778a-4f44-802a-cbc59a40c118 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0bd0dbea-778a-4f44-802a-cbc59a40c118');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "nMQbjes8xFU1",
        "outputId": "a26142ce-28ec-4bda-b07c-374276ed83af"
      },
      "source": [
        "p9.ggplot(response_dataframe, p9.aes('response')) + p9.geom_histogram(bins=20) + p9.ggtitle(\"Disease progression level\")"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (8736828651709)>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "iwvwjxb5xc-n",
        "outputId": "5c48430b-392c-4b0a-d879-b97192b110c8"
      },
      "source": [
        "import plotly.express as px\n",
        "fig = px.histogram(response_dataframe, \n",
        "                   x=\"response\", \n",
        "                   nbins=20,\n",
        "                   title=\"Disease progression level\")\n",
        "fig.show()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d6Si9f3LyDAO"
      },
      "source": [
        "## Exercise 1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "88xHPwu0yFyL"
      },
      "source": [
        "Using `sklearn`, fit a multivariate linear regression using all 442 samples to predict the response variable. Use all 10 predictors, and also score your model using predictions on all 442 samples."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1qJkOPjjyKBh",
        "outputId": "c75257de-607a-4c1c-b768-94f134e456e7"
      },
      "source": [
        "# Instantiate the model\n",
        "model = linear.LinearRegression()\n",
        "# Fit the pipeline\n",
        "model.fit(X=predictors, y=response)\n",
        "\n",
        "# Inspect the fit regression coefficients and intercept\n",
        "model.intercept_, model.coef_"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(152.1334841628965,\n",
              " array([ -10.01219782, -239.81908937,  519.83978679,  324.39042769,\n",
              "        -792.18416163,  476.74583782,  101.04457032,  177.06417623,\n",
              "         751.27932109,   67.62538639]))"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qgTUr_dlyOBe"
      },
      "source": [
        "Now score our model using the same dataset we used to fit the model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FC9TDK-OyQZt",
        "outputId": "3c2fd407-1d1e-448b-f523-ac265ea53deb"
      },
      "source": [
        "model.score(X=predictors, y=response)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5177494254132934"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "VT5bhS6Byja1",
        "outputId": "0004120a-180d-481c-de84-e76a6da6db28"
      },
      "source": [
        "import numpy as np\n",
        "response_predicted = model.predict(X=predictors)\n",
        "residuals = pd.DataFrame({'residuals': np.abs(response - response_predicted)})\n",
        "\n",
        "# the aes object contains only the variable that will be plotted on the x axis i.e. the one for which we want to generate a distribution\n",
        "p9.ggplot(residuals, p9.aes('residuals')) + p9.geom_histogram(bins=20) + p9.ggtitle(\"RESIDUALS\")"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (8736822651329)>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "EHo-AhM2RVds",
        "outputId": "0fcbdd2b-4659-42e3-cdc4-a39f90dcc667"
      },
      "source": [
        "p9.ggplot(response_dataframe, p9.aes('response')) + p9.geom_histogram(bins=20) + p9.ggtitle(\"Disease progression level\")"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (8736822652693)>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yVafaMoxysUX"
      },
      "source": [
        "## Exercise 2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H-M7ZbGNyudv"
      },
      "source": [
        "Do a test-train split, and run Exercise 1 again, this time fitting your model with the training set, and scoring it both with the test set and with the training set."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gbYQflyKcDyi"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dbIi6CHjyyEf"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "predictors_train, predictors_test, response_train, response_test = train_test_split(predictors, \n",
        "                                                                                    response,\n",
        "                                                                                    test_size=0.3, \n",
        "                                                                                    random_state=42, \n",
        "                                                                                    shuffle=True)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I18YYsDtzVHO",
        "outputId": "4cb03f8e-42bd-42dc-a2af-3625e82bf366"
      },
      "source": [
        "# Instantiate the model\n",
        "model = linear.LinearRegression()\n",
        "# Fit the pipeline\n",
        "model.fit(X=predictors_train, y=response_train)\n",
        "# Inspect the fit regression coefficients and intercept\n",
        "model.intercept_, model.coef_"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(151.00818273080338,\n",
              " array([  29.25034582, -261.70768053,  546.29737263,  388.40077257,\n",
              "        -901.95338706,  506.761149  ,  121.14845948,  288.02932495,\n",
              "         659.27133846,   41.37536901]))"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tdnVexiSzjEz",
        "outputId": "8436260e-7a6f-483e-b1a4-24b98b42a5be"
      },
      "source": [
        "model.score(X=predictors_test, y=response_test)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4772920174157329"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "q9nl7RP1zqXZ",
        "outputId": "4a07e979-e1d2-42b6-f7dc-6f10ba51c3e0"
      },
      "source": [
        "response_predicted = model.predict(X=predictors_test)\n",
        "residuals = pd.DataFrame({'residuals': np.abs(response_test - response_predicted)})\n",
        "p9.ggplot(residuals, p9.aes('residuals')) + p9.geom_histogram(bins=20) + p9.ggtitle(\"MLR - RESIDUALS\")"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (8736822548049)>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 464
        },
        "id": "Cu57STm4z37w",
        "outputId": "b9961660-815b-43c0-8576-656a265456f0"
      },
      "source": [
        "# Display the results as a scatter plot\n",
        "predictions = pd.DataFrame({'y': response_test, 'y_hat': response_predicted}) # plotnine works with pd.DataFrame\n",
        "\n",
        "(\n",
        "p9.ggplot(predictions, p9.aes('y', 'y_hat'))\n",
        "    + p9.geom_point()\n",
        "    + p9.scales.xlim(0, 400)\n",
        "    + p9.scales.ylim(0, 400)\n",
        ") # plotnine will emit a `PlotnineWarning` indicating that we have cropped some points from our plot"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (8736822521833)>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0_QxVfX20A0B"
      },
      "source": [
        "## Exercise 3"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IHGRPN3w0DyW"
      },
      "source": [
        "As with Exercise 2, but this time try using the default RandomForestRegressor instead. How do your results compare? Try just using one or two predictors."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4fvTIt810Hfw",
        "outputId": "143c9ecf-98d4-4bd6-99a8-612082967967"
      },
      "source": [
        "from sklearn import ensemble\n",
        "model = ensemble.RandomForestRegressor(n_estimators=100, max_depth=3)\n",
        "model.fit(X=predictors_train[[\"age\", \"bmi\"]], y=response_train)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(max_depth=3)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "zu5kTHpf0Pml",
        "outputId": "2c81e820-f18d-42d5-f5e8-dc70d1b5dddc"
      },
      "source": [
        "response_predicted = model.predict(X=predictors_test[[\"age\", \"bmi\"]])\n",
        "residuals = pd.DataFrame({'residuals': np.abs(response_test - response_predicted)})\n",
        "p9.ggplot(residuals, p9.aes('residuals')) + p9.geom_histogram(bins=20) + p9.ggtitle(\"RF - RESIDUALS\")"
      ],
      "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": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (8736822201741)>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "aUSR1XYbSV1j",
        "outputId": "e364dc24-d3fb-4d6c-f4f3-4b799c4f3f80"
      },
      "source": [
        "p9.ggplot(response_dataframe, p9.aes('response')) + p9.geom_histogram(bins=20) + p9.ggtitle(\"Disease progression level\")"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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z++23S5JWrlypZ599VsOHD1enTp0UGBioTz/9VC+++KJiY2P1s5/97Ky1JCUladKkSXr55Zc1YMAAjRgxwn2rdH5+vlauXKnWrVvX+b3V16hRo3TnnXcqPT1dn3/+uYYPH65LL71UBw8e1Keffqq33nqrRvg49Sydp59+WkVFRZozZ06N5+306tVLTzzxhGbNmqVrrrlG48aNU7t27ZSfn68dO3bob3/7m3bt2sXPM0LT8Oq9TkADOHW76KlfNpvNCgoKsrp06WKNHj3aWrVqlcetwKfUdrtrenq6NXjwYKtt27aW3W63IiIirISEBOu1116r8fVHjhyxHnnkEatr166Wv7+/FRQUZHXt2tW68847PW7draqqsp5++mmrS5culr+/v3XZZZdZv/71r60jR45Ykqzbb7+9Ubd/Nqduld65c6c1bdo09za7d+9urVq1qsb4Dh06WImJiWedb9euXdaYMWOs8PBwy8/Pz+rQoYM1ffp06+jRozXG7t+/35owYYIVEhJiBQYGWv369bM++OADa+TIkZbD4fAYe75bbktLS63f/e53VmxsrOVwOCyn02l17tzZmjBhgsftxmlpaVZiYqIVGRlp+fn5WZdeeqn1i1/8wnrnnXfcYz777DMrJSXF6tKli9W6dWvL6XRaXbt2tR599FHryJEj7nG19Y5lVf9dP/fcc1b37t0tf39/q3Xr1taAAQNqve35bPvzbHPXprZbpU9ZvXq11b9/f6tNmzaW3W632rdvb91000213upuWaf7wWazWfv27TvrNt9++21ryJAh1iWXXGL5+flZl112mTVgwABrwYIFVllZmXsct0qjMdksq55XhQEw2ty5czVv3jxlZ2c3m/8t/+QnP5HL5Trr9UAAIHHNCwAvKC0trbFu3bp12rVrlwYPHuyFigCYhGteADS5oUOHKioqSr169ZK/v78+/fRTrVy5UlFRUTUeRgcAZyK8AGhyQ4cO1cqVK7VhwwYdP35ckZGRmjRpkubNm6e2bdt6uzwAzRzXvAAAAKNwzQsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEuyue8FBQU1HmszWaTw+FQWVlZrT+BtSWx2+21/rDAloae8ERfVKMvTqMnqtETnhqiL8LDw+s0rsUfefHx8VFgYKB8fFr8rpC/v7+3S2gW6AlP9EU1+uI0eqIaPeGpKfuCPQ4AAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGMVmXYSPBSwqKqrzw3JsNpv7qYAX4a64IK1atVJlZaW3y/A6esITfVGNvjiNnqhGT3hqiL6o62f3RfnjASoqKur8iGJfX1/Z7XaVlJSoqqqqkStr3oKCglRcXOztMryOnvBEX1SjL06jJ6rRE54aoi/qGl44bQQAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCgX5d1GaDqpqamNMu/ChQsbZV4AgPk48gIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABglFbeLgCoTWpqaqPNvXDhwkabGwDQ+DjyAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGaVY/HqCoqEi//vWv1bZtWz3zzDOSpNzcXC1atEg5OTmKiorSXXfdpdjYWC9XCgAAvKVZHXl56aWX1L59e/dyZWWl5s+fr+uuu06vvvqqxo4dq7S0NB09etSLVQIAAG9qNuHlyy+/1MGDB3XjjTe61+3YsUMnTpxQcnKy/Pz8FB8fr+joaGVlZXmxUgAA4E3NIrycPHlSy5Yt069+9SvZbDb3+ry8PMXExMjH53SZHTt2VG5urjfKBAAAzUCzuOYlIyNDsbGxuuKKK7Rv3z73+rKyMjmdTo+xTqdT+fn5HusKCgpUUFDgXvbx8VFERESdtu3r6+vxe0tms9laxH4433ukJzy1lL44H/riNHqiGj3hqSn7wuvh5eDBg9q0aZP+9Kc/1XjN4XCopKTEY11JSYkcDofHuoyMDKWnp7uXU1JSNHXq1AuqIzg4+ILGX6zsdru3S2h0oaGhdRpHT5zWEvqiruiLavTEafTEaU3VF14PL7t371ZhYaF+9atfSZIqKipUUVGh2267Tffcc49yc3Plcrncp46ys7OVkJDgMceoUaOUmJjoXvbx8VFhYWGdtu/r66vg4GAVFRWpqqqqgd6VmZxOZ42weDE6X2/QE55aSl+cD31xGj1RjZ7w1BB9Udf/XHo9vPTr1089e/Z0L2/dulWbN2/WY489pqCgINntdq1du1bDhg3Txx9/rNzcXMXFxXnMER4ervDwcPdyQUHBBTdSVVVVi28+y7JaxD6o63ukJ6q1lL6oK/qCnjgTPVGtKfvC6+HF399f/v7+7mWn0ylfX193+po1a5YWL16sNWvWKDIyUjNnzlRISIi3ygUAAF7m9fByphtuuEE33HCDezkmJsb9wDoAAIBmcas0AABAXRFeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRmt2PBwAaW2pqaqPMu3DhwkaZFwDgiSMvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACj8JwXwAA8mwYATuPICwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRWnm7gMZgt9vl7+9fp7E2m02S5HQ6ZVlWY5bV7LVq1UpBQUHeLsNYJu67utRMX1Tje8Vp9EQ1esJTU/bFRRleKioqVFFRUaexvr6+stvtKikpUVVVVSNX1rwFBQWpuLjY22UYy8R9V5ea6YtqfK84jZ6oRk94aoi+qOuBB04bAQAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAY5aK8VRqeUlNTvV0CAAANhiMvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKK28XYAkLV68WJ988onKysoUFBSkQYMGacyYMZKk3NxcLVq0SDk5OYqKitJdd92l2NhYL1cMAAC8pVkcebnlllu0bNkyvfbaa0pLS9P777+vzMxMVVZWav78+bruuuv06quvauzYsUpLS9PRo0e9XTIAAPCSZhFeoqOj5e/v71622Ww6ePCgduzYoRMnTig5OVl+fn6Kj49XdHS0srKyvFgtAADwpmZx2kiSVqxYofXr1+vEiROKjIzUgAED9OGHHyomJkY+PqczVseOHZWbm+vxtQUFBSooKHAv+/j4KCIiok7b9fX19fgdqC8Te6guNdtsNiPfW0Pje8Vp9EQ1esJTU/ZFswkvt99+u2677Tbt3btX27Ztk9PpVFlZmZxOp8c4p9Op/Px8j3UZGRlKT093L6ekpGjq1KkXtP3g4OD6Fw9ICg0N9XYJF6yuNdvt9kauxBx8r6hGT5xGT5zWVH3RbMKLVJ3aunTpok8//VSvvvqqwsPDVVJS4jGmpKREDofDY92oUaOUmJjoXvbx8VFhYWGdtunr66vg4GAVFRWpqqrqx78JtFh17bnmpC41O53OGv8OWyK+V5xGT1SjJzw1RF/U9T9UzSq8nOJyuXTo0CH17NlTGRkZcrlc7lNH2dnZSkhI8BgfHh6u8PBw93JBQcEFN1JVVRXNhx/FxP6pS82WZRn53hoL3yvoiTPRE9Wasi+8fsHu8ePHtXnzZpWWlsrlcmnXrl3asGGDfvazn6l79+6y2+1au3atTp48qczMTOXm5iouLs7bZQMAAC9pFkde3nvvPf35z3+Wy+VSWFiYhg8frptvvlk2m02zZs3S4sWLtWbNGkVGRmrmzJkKCQnxdskAAMBLvB5eWrdurSeffPKsr8fExOiZZ55pwooAAEBz5vXTRgAAABeC8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYpZW3CwCAC5Gamtpocy9cuLDR5gbQcDjyAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCs95ARpIYz5/pLHwzBQAJuLICwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUS7KW6Xtdrv8/f3rNNZms0mSnE6nLMtqzLKAFiUoKMjbJVywxrx1/KWXXmq0uZtaq1atjPz7bWh8fnhqyr64KMNLRUWFKioq6jTW19dXdrtdJSUlqqqqauTKgJajuLjY2yU0KxfT/ggKCrqo3k998fnhqSH6oq4HHup12igpKUlfffVVra/t2bNHSUlJ9ZkWAADgvOoVXrZs2aKioqJaXysqKtIHH3zwo4oCAAA4m3pfsHvqXN+ZPvzwQ0VGRta7IAAAgHOp8zUvaWlpSktLk1QdXAYMGCAfH8/sc+LECVVWVuqee+5p2CoBAAD+vzqHl759++rBBx+UZVl6/PHHNW7cOLVr185jjN1uV7du3TR06NAGLxQAAEC6gPCSmJioxMRESdVHXu68805ddtlljVYYAABAbep1q/ScOXMaug4AAIA6qVd4cblcevHFF/X6669r//79Ki8v93jdZrPpm2++aZACAQAAfqhe4WXGjBlasGCBEhMTNWDAANnt9oauCwAAoFb1Ci+rVq3SvHnzNHv27IauBwAA4Jzq9ZyX8vJy9e3bt6FrAQAAOK96hZcJEybozTffbOhaAAAAzqtep4169+6tWbNm6fDhwxo4cKBCQkJqjBk5cuSPLg4AAOBM9QovkyZNkiTl5ubqtddeq/G6zWbjJ2wCAIBGUa/wkp2d3dB1ALjIpKamersENFON2RsLFy5stLnRfNQrvHTo0KGh6wAAAKiTeoWXvLy8846Jjo6uz9QAAADnVK/wEhMTI5vNds4xXPMCAAAaQ73Cy7p162qsKyws1MaNG7Vt2zY99dRTP7owAACA2tQrvAwbNqzW9SkpKZo+fbref/993XrrrT+qMAAAgNrU6yF15zJkyBCtWbOmoacFAACQ1Ajh5cMPP1RAQEBDTwsAACCpnqeNartHv6KiQrt371ZmZqYeeuihH10YAABAbeoVXmr7uUYBAQFq166dnn/+ed1xxx0/ujAAAIDa8IRdAABglAa/5gUAAKAx1Tu8fPbZZxo9erTatm0rf39/tW3bVmPGjNF//vOfhqwPAADAQ71OG23dulUDBw7UpZdeqnHjxikqKkqHDx/WunXr1KdPH7377rvq169fQ9cKAABQv/DyyCOPqH///lq/fr1atTo9xR/+8AfdfPPNeuSRR5SZmdlgRQIAAJxSr/Dy2Wef6fXXX/cILpLk6+ur1NRUJScnN0hxAADvqe2xGEBzUK9rXpxOp/Lz82t97fDhw3I6nT+qKAAAgLOpV3gZOnSoZsyYoffee89j/XvvvaeZM2fqlltuaZDiAAAAzlSv00YLFizQzp07NXjwYAUHBysyMlL5+fkqKirSz3/+cz3zzDN1nuvkyZNaunSpPv/8cxUXFys8PFxjxoxRYmKiJCk3N1eLFi1STk6OoqKidNdddyk2NrY+ZQMAgItAvcJLaGioPvroI61fv16ZmZkqLCxUWFiY+vXrp5tvvlk+PnU/oFNVVaWwsDA98cQTioqK0u7du/X4448rKipKnTt31vz58zVo0CClpaVp27ZtSktL09KlSxUSElKf0gEAgOHqFV42bdqkvLw8TZ48ucYpouXLl6tDhw4aMGBAneYKCAjQhAkT3MtXX321umjEHFQAABaBSURBVHXrpt27d6usrEwnTpxQcnKyfHx8FB8frzfffFNZWVm6+eab61M6AAAwXL3Cy6xZszRs2LBaX/vuu++Unp6urKysehVUXl6uvXv3aujQocrLy1NMTIzHkZyOHTsqNzfX42sKCgpUUFDgXvbx8VFERESdtufr6+vxOwA0Br7HNI2m3M98fniy2WxNti/qFV527typ+fPn1/paz5499eSTT9arGJfLpeeee05dunRRjx49tGfPnhp3LtV2p1NGRobS09PdyykpKZo6deoFbTs4OLheNQNAXYSGhjbKvOPHj2+UeU3VWPv5XPj8OM1utzfJduoVXmw2m44dO1bra4WFhaqqqrrgOS3L0vPPP68jR45o3rx5stlscjgcKikp8RhXUlIih8PhsW7UqFHuC3yl6iMvhYWFddqur6+vgoODVVRUVK+6AaAu6vo9CT9OU+5nPj88OZ3OGp/ZF6qu4bNe4eX666/XkiVLNHLkSNlsNvf6UwHk+uuvv6D5LMvS0qVLlZ2drfnz57vDSXR0tDIyMuRyudynjrKzs5WQkODx9eHh4QoPD3cvFxQUXHAjVVVV0XwAGg3fX5qGN/Yznx/VLMtqsv1Qr/Ayb948DRgwQD/96U+VkpKitm3b6uDBg1q5cqX27NmjLVu2XNB8y5Yt09dff60nnnhCgYGB7vXdu3eX3W7X2rVrNWzYMH388cfKzc1VXFxcfcoGAAAXgXqFlz59+mjTpk36zW9+oxkzZriPjJxa37t37zrPlZ+fr7feekt+fn765S9/6V6fnJysMWPGaNasWVq8eLHWrFmjyMhIzZw5k9ukAQBoweoVXiQpLi5OWVlZKisrU2FhoUJCQjyOmtRVZGSk/v73v5/19ZiYmAt66B0AALi41Tu8nOJwOGpcQAsAANBY6vWzjQAAALyF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGOVHP2EXAHB+qamp3i4BuGhw5AUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAorbxdQGOw2+3y9/ev01ibzSZJcjqdsiyrMcsCADSyoKCgJtsWnx+eWrVq1WT7/6IMLxUVFaqoqKjTWF9fX9ntdpWUlKiqqqqRKwMANKbi4uIm2xafH56CgoJ+9P6v64EHThsBAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMMpF+VOlAQAtU2pqaqPMu3DhwkaZF/XDkRcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABglFbeLmD9+vX65z//qZycHPXp00cPP/yw+7Xc3FwtWrRIOTk5ioqK0l133aXY2FgvVgsAALzN60dewsLCNGbMGA0aNMhjfWVlpebPn6/rrrtOr776qsaOHau0tDQdPXrUS5UCAIDmwOvhpW/fvurdu7eCg4M91u/YsUMnTpxQcnKy/Pz8FB8fr+joaGVlZXmpUgAA0Bx4PbycTV5enmJiYuTjc7rEjh07Kjc314tVAQAAb/P6NS9nU1ZWJqfT6bHO6XQqPz+/xtiCggIVFBS4l318fBQREVGn7fj6+nr8DgDAmWr7jODzw5PNZmuyfdFsw4vD4VBJSYnHupKSEjkcjhpjMzIylJ6e7l5OSUnR1KlTL2h7Z562AgDglHvvvbfR5l69enWjzd3U7HZ7k2yn2YaX6OhoZWRkyOVyuU8dZWdnKyEhocbYUaNGKTEx0b3s4+OjwsLCOm3H19dXwcHBKioqUlVVVcMUDwBAHdX186q5czqdNQ46XKjQ0NA6jfN6eKmqqlJVVZVcLpdcLpcqKirk4+Oj7t27y263a+3atRo2bJg+/vhj5ebmKi4ursYc4eHhCg8Pdy8XFBRccBA5VQcAAE3pYvnssSyryd6L18PLa6+9pjVr1riXs7KylJSUpAceeECzZs3S4sWLtWbNGkVGRmrmzJkKCQnxYrUAAMDbbJZlWd4uoqH98OLd8/H19VVoaKgKCwsvmvR7ptTUVG+XAAA4i4ULF3q7hAYRFBSk4uLiHzXHD8+inEuzvVUaAACgNoQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUr/9sI5zGY/wBADg/jrwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYpZW3C2gMdrtd/v7+dRprs9kkSU6nU5ZlNWZZAADUEBQU5O0SGkSrVq2a7L1clOGloqJCFRUVdRrr6+sru92ukpISVVVVNXJlAAB4Ki4u9nYJDSIoKOhHv5e6HnjgtBEAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEuylulAQBo6VJTUxtt7oULFzba3HXBkRcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAo7TydgEAALRkqamp3i7BOBx5AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhee8XCDuxwcAwLs48gIAAIxCeAEAAEYx4rTR8ePHtWTJEm3fvl0Oh0MjRozQsGHDvF0WAADwAiPCy7Jly3Ty5Em99NJLys/P1+zZs9WuXTtde+213i4NAAA0sWZ/2qi8vFxZWVmaNGmSAgMDFRMTo0GDBundd9/1dmkAAMALmv2RlwMHDsiyLHXo0MG97oorrtBHH33kXi4oKFBBQYF72cfHRxEREXWa39fX1+N3AABwbrV9Ztpstib7LG324aW8vFyBgYEe65xOp8rKytzLGRkZSk9Pdy+npKRo6tSpF7Sd4ODgOo1bvXr1Bc0LAEBLYbfbm2Q7zT68BAQEeAQVSSotLZXD4XAvjxo1SomJie5lHx8fFRYW1ml+X19fBQcHq6ioSFVVVQ1TtKGcTqdKSkq8XYbX0ROe6Itq9MVp9EQ1esJTQ/RFaGhoncY1+/By+eWXS5Ly8vIUHR0tScrOznb/WZLCw8MVHh7uXi4oKLjgRqqqqmrxzWdZVovfBz9ET1SjLzzRF/TEmeiJak3ZF83+gt2AgADFxcXp5ZdfVmlpqXJzc/XOO+9o4MCB3i4NAAB4QbM/8iJJd999txYvXqyUlBQ5HA6NGjWK26QBAGihjAgvrVu31iOPPOLtMgAAQDPQ7E8bAQAA/BDhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMYrMsy/J2Ed5UUFCgjIwMjRo1SuHh4d4uB80APYHa0Bc4Ez3hPS3+yEtBQYHS09NVUFDg7VLQTNATqA19gTPRE97T4sMLAAAwC+EFAAAYxXfu3LlzvV2EtzkcDvXq1UuBgYHeLgXNBD2B2tAXOBM94R0t/oJdAABgFk4bAQAAoxBeAACAUQgvAADAKK28XYA3HT9+XEuWLNH27dvlcDg0YsQIDRs2zNtloRGtX79e//znP5WTk6M+ffro4Ycfdr+Wm5urRYsWKScnR1FRUbrrrrsUGxvrfj0rK0srVqzQkSNH1LVrV6WmpioyMtIbbwMN6OTJk1q6dKk+//xzFRcXKzw8XGPGjFFiYqIk+qKlWrx4sT755BOVlZUpKChIgwYN0pgxYyTRE82C1YI988wz1vz5862SkhIrOzvbmjhxovXJJ594uyw0oqysLOujjz6yXnjhBev3v/+9e/3JkyetKVOmWK+99ppVUVFhffDBB9att95qFRYWWpZlWXl5edbo0aOt7du3W+Xl5VZ6err14IMPeuttoAGVlZVZr7zyinXo0CHL5XJZO3futG699VZr9+7d9EULlpuba5WXl1uWZVn5+fnWPffcY23dupWeaCZa7Gmj8vJyZWVladKkSQoMDFRMTIwGDRqkd99919uloRH17dtXvXv3VnBwsMf6HTt26MSJE0pOTpafn5/i4+MVHR2trKwsSdKWLVvUs2dP9ejRQ/7+/ho/fryys7OVl5fnjbeBBhQQEKAJEybo0ksvlc1m09VXX61u3bpp9+7d9EULFh0dLX9/f/eyzWbTwYMH6YlmosWGlwMHDsiyLHXo0MG97oorrqDBWqi8vDzFxMTIx+f0P4mOHTsqNzdXUvVh4iuuuML9WmBgoC699FL367h4lJeXa+/everQoQN90cKtWLFCo0eP1pQpU1ReXq4BAwbQE81Ei73mpby8vMZDhZxOp8rKyrxUEbyprKxMTqfTY53T6VR+fr6k6n6p7XX65eLicrn03HPPqUuXLurRo4f27NlDX7Rgt99+u2677Tbt3btX27Ztc//d0hPe12KPvAQEBNRoptLSUjkcDi9VBG9yOBwqKSnxWFdSUuLuh4CAAJWWlnq8Tr9cXCzL0vPPP68jR47o4Ycfls1moy8gm82mLl26yM/PT6+++io90Uy02PBy+eWXS5LHaaLs7GxFR0d7qyR4UXR0tHJzc+VyudzrsrOz3acVO3TooH379rlfKysr0//93/95nHaEuSzL0tKlS5Wdna25c+e6P2joC5zicrl06NAheqKZaLHhJSAgQHFxcXr55ZdVWlqq3NxcvfPOOxo4cKC3S0MjqqqqUkVFhVwul1wulyoqKlRZWanu3bvLbrdr7dq1OnnypDIzM5Wbm6u4uDhJUv/+/bV9+3b95z//UUVFhVavXq2YmBjC7kVi2bJl+vrrrzVv3jyP08n0Rct0/Phxbd68WaWlpXK5XNq1a5c2bNign/3sZ/REM9Gif7bR8ePHtXjxYvdzXkaOHMlzXi5yq1ev1po1azzWJSUl6YEHHlBOTo4WL16snJwcRUZG6u677/Z4dkNmZqZWrFihwsJCXXXVVbr//vt5dsNFID8/X3fccYf8/Pzk6+vrXp+cnKwxY8bQFy3Q8ePHlZaWpn379snlciksLEw33nijRo4cKZvNRk80Ay06vAAAAPO02NNGAADATIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwguAs7IsSydOnPB2GQDggfACwC0lJUXXXHON3nrrLcXGxsrf319vvvmmPvroIyUlJcnpdKpNmzYaP3688vPzPb72qaeeUufOnRUQEKCIiAjdeOONys7OliTl5OTIZrNpxYoVmjJlitq0aaOwsDBNnz5dlZWVHvPs2LFDgwcPdm8rOTlZeXl5HmNsNpt+//vfa+7cuYqKilJ4eLgmT56skpIS95ijR4/qzjvv1OWXX66AgAC1b99eY8eO9Zhn//79mjhxosLDw+VwOJSQkKBPP/20IXcpgEbQytsFAGheDh48qNTUVM2aNUvR0dHy8/NT//79NWTIEL322msqKSnRrFmzNGzYMH300UeSpJUrV2r27Nl6/PHH1adPHx07dkxbt25VUVGRx9y//e1vNWjQIP31r3/V9u3b9dhjj8lut+upp56SJH377bdKSEhQp06d9Morr6i8vFyPPvqoEhMT9cUXXygoKMg91+LFixUfH68VK1Zoz549evjhhxUVFeWea/r06dqwYYOeeuopxcTE6NChQ9qwYYP76wsLC9WvXz+1bt1aixYtUps2bbRo0SIlJSXpv//9Lz8FGGjOLAD4/26//XZLkrVt2zb3uoSEBKtv376Wy+Vyr9u5c6dls9msf/zjH5ZlWda9995r9ezZ86zzZmdnW5Ks+Ph4j/WzZ8+2AgMDrSNHjliWZVnTpk2znE6n9f3337vH7N6927LZbNbChQvd6yRZ1113XY3aO3Xq5F7+yU9+Yk2fPv2sNT322GNWmzZtrMOHD7vXlZeXW9HR0dbDDz981q8D4H2cNgLg4ZJLLtH1118vSSotLVVWVpZGjx6tqqoqVVZWqrKyUldeeaXat2+vf//735Kknj176rPPPtP06dOVmZmpkydP1jr3iBEjPJaTk5NVWlqqHTt2SJK2bt2qpKQkhYWFucd07dpVsbGxyszM9PjagQMHeixfffXV2r9/v3u5Z8+eWr58uZ555hl9+eWXNWp55513NGDAAIWFhbnfl6+vrxITE93vC0DzRHgB4CEqKsr958LCQlVVVWnatGny8/Pz+JWXl6dvv/1WUvW1Mn/84x+1ceNGxcfHKyIiQvfff7/Kyso85j7zVMypbR06dMi9vR9u/4fjjhw54rEuJCTEY9lut3tcXLxo0SJNmjRJCxYsUPfu3RUdHa0XXnjB/XpBQYHeeOONGu/r5Zdfdr8vAM0T17wA8GCz2dx/DgkJkc1m029/+1sNHz68xtjw8HBJko+Pj+6//37df//9OnDggNasWaNHHnlE4eHhmj17tnv8mRf5Hj58WJLUtm1bSVJYWFiNMafGXXnllRf0Ptq0aaPnnntOzz33nHbs2KE//elPuueee3TNNdcoPj5eYWFh+sUvfqH58+fX+Fp/f/8L2haApsWRFwBn5XQ61adPH+3evVu9evWq8SsmJqbG11x++eV68MEH9dOf/lS7d+/2eG3dunUey6+//roCAwPVvXt3SVK/fv20adMmFRYWusd8/fXX+uKLL9SvX796v4/u3bvrj3/8oyS5a7rxxhu1a9cudevWrcb7OlUPgOaJIy8AzukPf/iDkpKSdOutt2rs2LEKDQ3V/v379e6772ry5Mnq37+/7r77boWGhqp3794KDQ1VVlaWPv/8c91zzz0ec33zzTeaPHmyxo4dq+3btystLU3Tpk1TaGioJGnatGl66aWXNGjQID366KMqLy933/WUkpJyQXXHxcVpxIgRuuaaa+Tr66uVK1fKbrcrPj5eUvXdSKtWrVJiYqLuv/9+RUdH67vvvtPHH3+syy67TNOmTWuQ/Qeg4RFeAJxT3759lZmZqTlz5mjy5MmqqKhQu3btdMMNN6hz587uMenp6UpPT1dpaak6duyoP/7xj5oyZYrHXE8++aS2bNmi0aNHy9fXV/fee6+efPJJ9+vt27fX+++/r4ceekgTJkyQr6+vBg4cqGeffdbjNum6iIuL08qVK5WdnS0fHx91795db775prp16yap+sLkbdu2adasWZoxY4a+//57RUZGqnfv3jUuLAbQvNgsy7K8XQSAi1tOTo6uuOIK/e///q+Sk5O9XQ4Aw3HNCwAAMArhBQAAGIXTRgAAwCgceQEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjPL/ANUwJ36J1ONzAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (8736822127413)>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fU9Kev_O0ZTL",
        "outputId": "c5e5f549-bac7-4319-da1c-a3c19c0eddf5"
      },
      "source": [
        "model.score(X=predictors_test[[\"age\", \"bmi\"]], y=response_test)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.2872737191015292"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        },
        "id": "YFFhAGQ90foa",
        "outputId": "3e9368e4-2983-4806-d3b6-915da518f37f"
      },
      "source": [
        "predictions = pd.DataFrame({'y': response_test, 'y_hat': response_predicted}) # plotnine works with pd.DataFrame\n",
        "\n",
        "(\n",
        "p9.ggplot(predictions, p9.aes('y', 'y_hat'))\n",
        "    + p9.geom_point()\n",
        "    + p9.scales.xlim(0, 300)\n",
        "    + p9.scales.ylim(0, 300)\n",
        ")"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/plotnine/layer.py:401: PlotnineWarning:\n",
            "\n",
            "geom_point : Removed 1 rows containing missing values.\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (8736822165161)>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hrEQeHmZ1OUY"
      },
      "source": [
        "## Exercise 4"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wJvPTjHW1QMv"
      },
      "source": [
        "Dive into the documentation of RandomForestRegressor and try tweaking some of the hyperparameters to see how this affects your model score on the training and test datasets ('n_estimators' and 'max_depth' are good places to start, more on this next week!)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MhjCkcZG1RUe"
      },
      "source": [
        "n_estimators_min, n_estimators_max = 2, 100\n",
        "max_depth_min, max_depth_max = 2, 10"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FQD-YmOQ1U6l"
      },
      "source": [
        "grid_parameters = np.mgrid[n_estimators_min - 1:n_estimators_max + 1:20j,  max_depth_min - 1:max_depth_max + 1:9j]\n",
        "parameters = grid_parameters.reshape((2, -1)).T"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2e8ySx411WoS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5af990b1-70da-4301-daec-8383652ef4fa"
      },
      "source": [
        "scores = np.empty(parameters.shape[0])\n",
        "for i, (n_estimators, max_depth) in enumerate(parameters):\n",
        "    print(\"i: \", i)\n",
        "    print(\"n_estimators: \", int(n_estimators))\n",
        "    print(\"max_depth: \", int(max_depth))\n",
        "    model = ensemble.RandomForestRegressor(n_estimators=int(n_estimators), max_depth=int(max_depth), random_state = np.random.seed(1234))\n",
        "    model.fit(X=predictors_train, y=response_train)\n",
        "    score = model.score(X=predictors_test, y=response_test)\n",
        "    print(\"score: \", score)\n",
        "    scores[i] = score"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "i:  0\n",
            "n_estimators:  1\n",
            "max_depth:  1\n",
            "score:  0.3986646512444215\n",
            "i:  1\n",
            "n_estimators:  1\n",
            "max_depth:  2\n",
            "score:  0.306769087559461\n",
            "i:  2\n",
            "n_estimators:  1\n",
            "max_depth:  3\n",
            "score:  0.23537940596017193\n",
            "i:  3\n",
            "n_estimators:  1\n",
            "max_depth:  4\n",
            "score:  0.2551777542118129\n",
            "i:  4\n",
            "n_estimators:  1\n",
            "max_depth:  6\n",
            "score:  0.04294079199629808\n",
            "i:  5\n",
            "n_estimators:  1\n",
            "max_depth:  7\n",
            "score:  -0.058883156084337474\n",
            "i:  6\n",
            "n_estimators:  1\n",
            "max_depth:  8\n",
            "score:  -0.019560471309697514\n",
            "i:  7\n",
            "n_estimators:  1\n",
            "max_depth:  9\n",
            "score:  0.11860145113733145\n",
            "i:  8\n",
            "n_estimators:  1\n",
            "max_depth:  11\n",
            "score:  -0.08161897157565323\n",
            "i:  9\n",
            "n_estimators:  6\n",
            "max_depth:  1\n",
            "score:  0.3780196737636643\n",
            "i:  10\n",
            "n_estimators:  6\n",
            "max_depth:  2\n",
            "score:  0.449982748449425\n",
            "i:  11\n",
            "n_estimators:  6\n",
            "max_depth:  3\n",
            "score:  0.4879548973404235\n",
            "i:  12\n",
            "n_estimators:  6\n",
            "max_depth:  4\n",
            "score:  0.51327702639602\n",
            "i:  13\n",
            "n_estimators:  6\n",
            "max_depth:  6\n",
            "score:  0.42190409090172387\n",
            "i:  14\n",
            "n_estimators:  6\n",
            "max_depth:  7\n",
            "score:  0.3819892642997613\n",
            "i:  15\n",
            "n_estimators:  6\n",
            "max_depth:  8\n",
            "score:  0.392119793396406\n",
            "i:  16\n",
            "n_estimators:  6\n",
            "max_depth:  9\n",
            "score:  0.3640659759806115\n",
            "i:  17\n",
            "n_estimators:  6\n",
            "max_depth:  11\n",
            "score:  0.3572782412013584\n",
            "i:  18\n",
            "n_estimators:  11\n",
            "max_depth:  1\n",
            "score:  0.36984532171112483\n",
            "i:  19\n",
            "n_estimators:  11\n",
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            "score:  0.4629846596728272\n",
            "i:  160\n",
            "n_estimators:  90\n",
            "max_depth:  9\n",
            "score:  0.45820420585596977\n",
            "i:  161\n",
            "n_estimators:  90\n",
            "max_depth:  11\n",
            "score:  0.45731774282135873\n",
            "i:  162\n",
            "n_estimators:  95\n",
            "max_depth:  1\n",
            "score:  0.33234132555916907\n",
            "i:  163\n",
            "n_estimators:  95\n",
            "max_depth:  2\n",
            "score:  0.4539648442507722\n",
            "i:  164\n",
            "n_estimators:  95\n",
            "max_depth:  3\n",
            "score:  0.4858605786307656\n",
            "i:  165\n",
            "n_estimators:  95\n",
            "max_depth:  4\n",
            "score:  0.4859807403507701\n",
            "i:  166\n",
            "n_estimators:  95\n",
            "max_depth:  6\n",
            "score:  0.46555706923315743\n",
            "i:  167\n",
            "n_estimators:  95\n",
            "max_depth:  7\n",
            "score:  0.4568607350281628\n",
            "i:  168\n",
            "n_estimators:  95\n",
            "max_depth:  8\n",
            "score:  0.46265549835428155\n",
            "i:  169\n",
            "n_estimators:  95\n",
            "max_depth:  9\n",
            "score:  0.4584366344820383\n",
            "i:  170\n",
            "n_estimators:  95\n",
            "max_depth:  11\n",
            "score:  0.4559414511135862\n",
            "i:  171\n",
            "n_estimators:  101\n",
            "max_depth:  1\n",
            "score:  0.3317501676378305\n",
            "i:  172\n",
            "n_estimators:  101\n",
            "max_depth:  2\n",
            "score:  0.4551637184761236\n",
            "i:  173\n",
            "n_estimators:  101\n",
            "max_depth:  3\n",
            "score:  0.48594862542932993\n",
            "i:  174\n",
            "n_estimators:  101\n",
            "max_depth:  4\n",
            "score:  0.48579525582529404\n",
            "i:  175\n",
            "n_estimators:  101\n",
            "max_depth:  6\n",
            "score:  0.4631861077827467\n",
            "i:  176\n",
            "n_estimators:  101\n",
            "max_depth:  7\n",
            "score:  0.4564377541092187\n",
            "i:  177\n",
            "n_estimators:  101\n",
            "max_depth:  8\n",
            "score:  0.4612055970394475\n",
            "i:  178\n",
            "n_estimators:  101\n",
            "max_depth:  9\n",
            "score:  0.456354184239686\n",
            "i:  179\n",
            "n_estimators:  101\n",
            "max_depth:  11\n",
            "score:  0.4539212968171259\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6b_UGMv8Tntu",
        "outputId": "cbc09c81-896c-4b95-92c5-f2838d68c66d"
      },
      "source": [
        "scores.max()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.51327702639602"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BmiQH-cPV-uM",
        "outputId": "baccb8e4-c860-4cc1-ba38-d89f3658bec3"
      },
      "source": [
        "scores"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.39866465,  0.30676909,  0.23537941,  0.25517775,  0.04294079,\n",
              "       -0.05888316, -0.01956047,  0.11860145, -0.08161897,  0.37801967,\n",
              "        0.44998275,  0.4879549 ,  0.51327703,  0.42190409,  0.38198926,\n",
              "        0.39211979,  0.36406598,  0.35727824,  0.36984532,  0.45076508,\n",
              "        0.48365181,  0.48630423,  0.43013856,  0.41071877,  0.43352614,\n",
              "        0.40364451,  0.38814951,  0.35535092,  0.45964119,  0.48997202,\n",
              "        0.47389305,  0.44205944,  0.43364775,  0.44075721,  0.41579974,\n",
              "        0.42272316,  0.36088736,  0.45430984,  0.49730732,  0.47955024,\n",
              "        0.44787735,  0.44400255,  0.44888583,  0.42785219,  0.4238103 ,\n",
              "        0.36973337,  0.45161101,  0.49099907,  0.47719431,  0.4431747 ,\n",
              "        0.43375325,  0.4387848 ,  0.42032865,  0.41937248,  0.37835467,\n",
              "        0.46024726,  0.4904854 ,  0.47720077,  0.44610033,  0.44088925,\n",
              "        0.44344232,  0.42160329,  0.43393388,  0.37044703,  0.46568257,\n",
              "        0.49410495,  0.4817664 ,  0.45043193,  0.44709183,  0.45052351,\n",
              "        0.42905799,  0.44219121,  0.36336611,  0.46182287,  0.49126952,\n",
              "        0.48177684,  0.45022255,  0.44760489,  0.45870332,  0.435989  ,\n",
              "        0.44506649,  0.36760613,  0.4629241 ,  0.4926657 ,  0.48339787,\n",
              "        0.45018229,  0.44703359,  0.45794779,  0.4386014 ,  0.44437185,\n",
              "        0.35745328,  0.46061735,  0.49020162,  0.48190004,  0.44953863,\n",
              "        0.44546516,  0.45532053,  0.43948013,  0.44572749,  0.3543453 ,\n",
              "        0.45647956,  0.48788432,  0.483378  ,  0.45657363,  0.45202723,\n",
              "        0.46222809,  0.44550457,  0.44884146,  0.34533879,  0.45465285,\n",
              "        0.48796079,  0.48178128,  0.45954288,  0.45668753,  0.46312795,\n",
              "        0.45024271,  0.45254394,  0.34327497,  0.45570192,  0.48941221,\n",
              "        0.48544092,  0.46314465,  0.4600981 ,  0.46635334,  0.45576383,\n",
              "        0.45565125,  0.3359304 ,  0.45406329,  0.48854589,  0.48574853,\n",
              "        0.46411413,  0.45992822,  0.46779483,  0.45934709,  0.45865756,\n",
              "        0.33602376,  0.45520074,  0.48743985,  0.48571337,  0.46560079,\n",
              "        0.46180814,  0.4670305 ,  0.46243153,  0.45959261,  0.329224  ,\n",
              "        0.45541854,  0.4889161 ,  0.48794847,  0.46879016,  0.46038679,\n",
              "        0.46606314,  0.46160722,  0.46134628,  0.33304343,  0.45374495,\n",
              "        0.48689349,  0.48609339,  0.46636288,  0.45639137,  0.46298466,\n",
              "        0.45820421,  0.45731774,  0.33234133,  0.45396484,  0.48586058,\n",
              "        0.48598074,  0.46555707,  0.45686074,  0.4626555 ,  0.45843663,\n",
              "        0.45594145,  0.33175017,  0.45516372,  0.48594863,  0.48579526,\n",
              "        0.46318611,  0.45643775,  0.4612056 ,  0.45635418,  0.4539213 ])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pR27CucITzYL",
        "outputId": "75be3337-3a6f-48cc-c306-0ab397cae923"
      },
      "source": [
        "np.where(scores == scores.max())"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([12]),)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gYEfBMtf1YXF"
      },
      "source": [
        "scores = np.reshape(scores, (20, 9))"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "id": "zyVsPTgN1aKS",
        "outputId": "2c084439-a62a-4f31-875b-4221ba5d0e19"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fig = plt.figure(figsize=(6, 4.5))\n",
        "ax = fig.add_subplot()\n",
        "ax.set_ylabel('max_depth')\n",
        "ax.set_xlabel('n_estimators')\n",
        "score_min, score_max = scores.min(), scores.max()\n",
        "plot = ax.imshow(\n",
        "    np.rot90(scores),\n",
        "    vmin=score_min,\n",
        "    vmax=score_max,\n",
        "    aspect='auto',\n",
        "    extent=(n_estimators_min - 1, n_estimators_max, max_depth_min - 1, max_depth_max),\n",
        "    cmap=plt.cm.viridis)\n",
        "\n",
        "fig.colorbar(plot, ax=ax)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.colorbar.Colorbar at 0x7f2332c48350>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x324 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m3n28zkb1wQb",
        "outputId": "a8e4f53f-b8d3-4969-ffb1-3e89a7d06bd3"
      },
      "source": [
        "model = ensemble.RandomForestRegressor(n_estimators=6, max_depth=4, random_state = np.random.seed(1234))\n",
        "model.fit(X=predictors_train, y=response_train)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(max_depth=4, n_estimators=6)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MDxEO8lY11nX",
        "outputId": "6063f29d-f366-411b-cb7a-433a812e420f"
      },
      "source": [
        "model.score(X=predictors_test, y=response_test)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.51327702639602"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3iOF-QDZ31Tz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "769cef35-6a13-4459-e691-15e0ae941cd3"
      },
      "source": [
        "from scipy import stats\n",
        "\n",
        "df = predictors_train\n",
        "z_scores = stats.zscore(df)\n",
        "abs_z_scores = np.abs(z_scores)\n",
        "filtered_entries = (abs_z_scores < 3).all(axis=1)\n",
        "new_predictors_train = df[filtered_entries]\n",
        "\n",
        "new_response_train = response_train[filtered_entries]\n",
        "\n",
        "new_predictors_train.shape"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(300, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hX1UQv1Q4gco",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fb5f6bce-3ca5-4ed6-82a5-d807fcdbe435"
      },
      "source": [
        "predictors_train.shape"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(309, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aN1TE0404sKB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7ac285cb-70b4-479a-b49e-424e771f4bd3"
      },
      "source": [
        "model = ensemble.RandomForestRegressor(n_estimators=5, max_depth=3)\n",
        "model.fit(X=new_predictors_train, y=new_response_train)\n",
        "model.score(X=predictors_test, y=response_test)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4903500981232257"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    }
  ]
}