decision_trees.ipynb

{
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  "metadata": {
    "colab": {
      "name": "decision trees.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "fPll4nBAoNaq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import sklearn\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn import metrics"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oLQxVxBwqq5C",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "w_df=pd.read_csv(\"winequality-red.csv\",delimiter=';')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1-qk6VHFq1zj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "outputId": "f94b9bee-b20d-4485-e5c2-261ee3bea14c"
      },
      "source": [
        "w_df"
      ],
      "execution_count": 554,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.700</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.99780</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.880</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.6</td>\n",
              "      <td>0.098</td>\n",
              "      <td>25.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0.99680</td>\n",
              "      <td>3.20</td>\n",
              "      <td>0.68</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.760</td>\n",
              "      <td>0.04</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.092</td>\n",
              "      <td>15.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0.99700</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.65</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.2</td>\n",
              "      <td>0.280</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.075</td>\n",
              "      <td>17.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.99800</td>\n",
              "      <td>3.16</td>\n",
              "      <td>0.58</td>\n",
              "      <td>9.8</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.700</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.99780</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1594</th>\n",
              "      <td>6.2</td>\n",
              "      <td>0.600</td>\n",
              "      <td>0.08</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.090</td>\n",
              "      <td>32.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>0.99490</td>\n",
              "      <td>3.45</td>\n",
              "      <td>0.58</td>\n",
              "      <td>10.5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1595</th>\n",
              "      <td>5.9</td>\n",
              "      <td>0.550</td>\n",
              "      <td>0.10</td>\n",
              "      <td>2.2</td>\n",
              "      <td>0.062</td>\n",
              "      <td>39.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>0.99512</td>\n",
              "      <td>3.52</td>\n",
              "      <td>0.76</td>\n",
              "      <td>11.2</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1596</th>\n",
              "      <td>6.3</td>\n",
              "      <td>0.510</td>\n",
              "      <td>0.13</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.076</td>\n",
              "      <td>29.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.99574</td>\n",
              "      <td>3.42</td>\n",
              "      <td>0.75</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1597</th>\n",
              "      <td>5.9</td>\n",
              "      <td>0.645</td>\n",
              "      <td>0.12</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.075</td>\n",
              "      <td>32.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>0.99547</td>\n",
              "      <td>3.57</td>\n",
              "      <td>0.71</td>\n",
              "      <td>10.2</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1598</th>\n",
              "      <td>6.0</td>\n",
              "      <td>0.310</td>\n",
              "      <td>0.47</td>\n",
              "      <td>3.6</td>\n",
              "      <td>0.067</td>\n",
              "      <td>18.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>0.99549</td>\n",
              "      <td>3.39</td>\n",
              "      <td>0.66</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1599 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      fixed acidity  volatile acidity  citric acid  ...  sulphates  alcohol  quality\n",
              "0               7.4             0.700         0.00  ...       0.56      9.4        5\n",
              "1               7.8             0.880         0.00  ...       0.68      9.8        5\n",
              "2               7.8             0.760         0.04  ...       0.65      9.8        5\n",
              "3              11.2             0.280         0.56  ...       0.58      9.8        6\n",
              "4               7.4             0.700         0.00  ...       0.56      9.4        5\n",
              "...             ...               ...          ...  ...        ...      ...      ...\n",
              "1594            6.2             0.600         0.08  ...       0.58     10.5        5\n",
              "1595            5.9             0.550         0.10  ...       0.76     11.2        6\n",
              "1596            6.3             0.510         0.13  ...       0.75     11.0        6\n",
              "1597            5.9             0.645         0.12  ...       0.71     10.2        5\n",
              "1598            6.0             0.310         0.47  ...       0.66     11.0        6\n",
              "\n",
              "[1599 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 554
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GbNY_Y-G2f4Q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "4651da23-745b-4878-8db8-b3f8dd93d68c"
      },
      "source": [
        "w_df.dtypes"
      ],
      "execution_count": 555,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "fixed acidity           float64\n",
              "volatile acidity        float64\n",
              "citric acid             float64\n",
              "residual sugar          float64\n",
              "chlorides               float64\n",
              "free sulfur dioxide     float64\n",
              "total sulfur dioxide    float64\n",
              "density                 float64\n",
              "pH                      float64\n",
              "sulphates               float64\n",
              "alcohol                 float64\n",
              "quality                   int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 555
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LYaY6ox6rVye",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "red_x_train,red_x_test,red_y_train,red_y_test = train_test_split(w_df.drop(columns=\"quality\"),w_df['quality'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cpeQfvGmzy27",
        "colab_type": "text"
      },
      "source": [
        "Should we use regressor or classifier(Decision Tree)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LCRwuleT0yeA",
        "colab_type": "text"
      },
      "source": [
        "We will use clasifier here."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yikAWQr2r5SQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "outputId": "37fa3642-5dd2-442c-8046-871564489c3a"
      },
      "source": [
        "tree = DecisionTreeClassifier(criterion='entropy',max_depth=4, random_state=1)\n",
        "tree.fit(red_x_train,red_y_train)"
      ],
      "execution_count": 557,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy',\n",
              "                       max_depth=4, max_features=None, max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                       random_state=1, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 557
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pen8ktjQtFWp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred=tree.predict(red_x_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nY8RJWD2tYpt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c4e186bb-00ab-48f0-d98c-f2e67acbb809"
      },
      "source": [
        "print(\"Accuracy:\",metrics.accuracy_score(red_y_test, y_pred))"
      ],
      "execution_count": 559,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 0.595\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "munhCXDouHOK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "436a599b-bf25-431d-8668-ae1cac806ce4"
      },
      "source": [
        "tree = DecisionTreeClassifier(criterion=\"entropy\",max_depth=10, random_state=0)\n",
        "tree.fit(red_x_train,red_y_train)\n",
        "y_pred=tree.predict(red_x_test)\n",
        "print(\"Accuracy:\",metrics.accuracy_score(red_y_test, y_pred))"
      ],
      "execution_count": 560,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 0.5875\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xnU6WnCy0Can",
        "colab_type": "text"
      },
      "source": [
        "Try several ways for max_depth. What is the best.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lB8yp0CG0LhL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d76050c8-2d08-4f71-fd1c-83a7a4c4af68"
      },
      "source": [
        "tree = DecisionTreeClassifier(max_depth=10,random_state=0)\n",
        "tree.fit(red_x_train,red_y_train)\n",
        "y_pred=tree.predict(red_x_test)\n",
        "print(\"Accuracy:\",metrics.accuracy_score(red_y_test, y_pred))"
      ],
      "execution_count": 561,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 0.58\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4lSwhXY90qdD",
        "colab_type": "text"
      },
      "source": [
        "What is accuracy."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_OgISxa73nCI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7e1f4332-75b6-4dc5-f847-d0813825b668"
      },
      "source": [
        "utfall = (red_rf_y_results['prediction'] == red_rf_y_results['quality']).value_counts()\n",
        "print(\"The decision tree predicts the test data in\", (utfall[1]/(utfall[0]+utfall[1]))*100 , \"% of the cases.\")"
      ],
      "execution_count": 562,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The decision tree predicts the test data in 65.25 % of the cases.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CMTUUegcyHIM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "3eeeb937-c984-4aa7-a025-7251fdcaeec9"
      },
      "source": [
        "red_rf_y_results=pd.DataFrame(red_y_test)\n",
        "red_rf_y_results['prediction'] = tree.predict(red_x_test)\n",
        "red_rf_y_results['error'] = red_rf_y_results['prediction'] - red_rf_y_results['quality']\n",
        "red_rf_y_results.tail()"
      ],
      "execution_count": 563,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>quality</th>\n",
              "      <th>prediction</th>\n",
              "      <th>error</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1090</th>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>-2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>313</th>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1583</th>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>787</th>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      quality  prediction  error\n",
              "1090        8           6     -2\n",
              "130         5           5      0\n",
              "313         5           5      0\n",
              "1583        5           5      0\n",
              "787         6           6      0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 563
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6bjhyVwSxrRa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2eedf174-bf00-49d3-dd1a-72afff6f4d9e"
      },
      "source": [
        "print(\"Accuracy:\",metrics.accuracy_score(red_rf_y_results['prediction'], red_rf_y_results['quality']))"
      ],
      "execution_count": 564,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 0.58\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-pHnqop_u2E3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "omJJHenJ0_7W",
        "colab_type": "text"
      },
      "source": [
        "Random Forest (Tasks same as above). What is the accuracy."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gJy1oI4eu2Iu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.ensemble import RandomForestRegressor\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JkeGSZqLvBj7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        },
        "outputId": "fb319426-51f5-475a-cdd1-7c3d8f2a4961"
      },
      "source": [
        "w_rf= RandomForestClassifier(n_estimators=50)\n",
        "w_rf.fit(red_x_train,red_y_train)"
      ],
      "execution_count": 566,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=None, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=50,\n",
              "                       n_jobs=None, oob_score=False, random_state=None,\n",
              "                       verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 566
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_CVNgHKXvPRo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "c2cf3717-623f-4a63-8349-74d7d740e891"
      },
      "source": [
        "red_rf_y_results=pd.DataFrame(red_y_test)\n",
        "red_rf_y_results['prediction'] = w_rf.predict(red_x_test)\n",
        "red_rf_y_results['error'] = red_rf_y_results['prediction'] - red_rf_y_results['quality']\n",
        "red_rf_y_results.tail()"
      ],
      "execution_count": 567,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>quality</th>\n",
              "      <th>prediction</th>\n",
              "      <th>error</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1090</th>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>-2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>313</th>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1583</th>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>787</th>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      quality  prediction  error\n",
              "1090        8           6     -2\n",
              "130         5           5      0\n",
              "313         5           5      0\n",
              "1583        5           5      0\n",
              "787         6           6      0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 567
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ijK12HCzPCr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "ec02161a-7e4c-4a00-eb8d-0624bfd6f9ee"
      },
      "source": [
        "w_rf= RandomForestClassifier(n_estimators=50)\n",
        "w_rf.fit(red_x_train,red_y_train)\n",
        "red_rf_y_results=pd.DataFrame(red_y_test)\n",
        "red_rf_y_results['prediction'] = w_rf.predict(red_x_test)\n",
        "red_rf_y_results['error'] = red_rf_y_results['prediction'] - red_rf_y_results['quality']\n",
        "red_rf_y_results.tail()"
      ],
      "execution_count": 568,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>quality</th>\n",
              "      <th>prediction</th>\n",
              "      <th>error</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1090</th>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>313</th>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1583</th>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>787</th>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      quality  prediction  error\n",
              "1090        8           7     -1\n",
              "130         5           5      0\n",
              "313         5           5      0\n",
              "1583        5           5      0\n",
              "787         6           6      0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 568
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tRilHBGV1Mrr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ab4bc14d-58f6-42b8-e498-b57d7c6408a7"
      },
      "source": [
        "print(\"Accuracy:\",metrics.accuracy_score(red_rf_y_results['prediction'], red_rf_y_results['quality']))"
      ],
      "execution_count": 569,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 0.695\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A73tGJ571TFw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "88042267-82f3-4249-f81b-55e6bb7a9781"
      },
      "source": [
        "w_rf= RandomForestClassifier(n_estimators=200,random_state=0)\n",
        "w_rf.fit(red_x_train,red_y_train)\n",
        "red_rf_y_results=pd.DataFrame(red_y_test)\n",
        "red_rf_y_results['prediction'] = w_rf.predict(red_x_test)\n",
        "red_rf_y_results['error'] = red_rf_y_results['prediction'] - red_rf_y_results['quality']\n",
        "red_rf_y_results.tail()\n",
        "print(\"Accuracy:\",metrics.accuracy_score(red_rf_y_results['prediction'], red_rf_y_results['quality']))"
      ],
      "execution_count": 570,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 0.6875\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mfx39Dv_2so2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8f7dec02-c5c5-4e2f-8d41-107f211254e0"
      },
      "source": [
        "w_rf= RandomForestClassifier(n_estimators=200,random_state=2)\n",
        "w_rf.fit(red_x_train,red_y_train)\n",
        "red_rf_y_results=pd.DataFrame(red_y_test)\n",
        "red_rf_y_results['prediction'] = w_rf.predict(red_x_test)\n",
        "red_rf_y_results['error'] = red_rf_y_results['prediction'] - red_rf_y_results['quality']\n",
        "red_rf_y_results.tail()\n",
        "print(\"Accuracy:\",metrics.accuracy_score(red_rf_y_results['prediction'], red_rf_y_results['quality']))"
      ],
      "execution_count": 571,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 0.685\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Zua_K6m30he",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d7855483-6ec6-417a-84fa-375dc4f5559f"
      },
      "source": [
        "utfall = (red_rf_y_results['prediction'] == red_rf_y_results['quality']).value_counts()\n",
        "print(\"The random forest predicts the test data in\", (utfall[1]/(utfall[0]+utfall[1]))*100 , \"% of the cases.\")"
      ],
      "execution_count": 572,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The random forest predicts the test data in 68.5 % of the cases.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eu3zUxXY4Q8z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aA-y8dFs4Rrq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Ta4f4zS4Ry8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yilSu06V4R6V",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CHQf_7zL4R9y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F9jEzzsD4SKm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h_mIJqM84R34",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U04_uTXs4Rvs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JCpr0LjZ4Rkn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xdbK6hl24Rhx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I_EXVhGN4RYV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M9XQKr2C4RWa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9T1Y9wyp4ROh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IKDwWk7D4RG9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PV1dY5tP4REJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}