{"cells":[{"cell_type":"markdown","source":"### Árbol de Decisión modelo de Clasificación en Scikit-Learn - Titanic","metadata":{"id":"-HLtE6c9Gz0O","cell_id":"afe7aa1375e5445da13f6c446d5cd529","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"from google.colab import drive\nimport os\ndrive.mount('/content/gdrive')\n# Establecer ruta de acceso en dr\nimport os\nprint(os.getcwd())\nos.chdir(\"/content/gdrive/My Drive\")","metadata":{"id":"_13vHnKbG2I0","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"91fccf2bdd084d75a43e72a42349812d","outputId":"caba344c-2f29-4d98-bd5e-6d057edaec2e","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":3540,"user_tz":240,"timestamp":1650987494738},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stdout","text":"Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n/content/gdrive/My Drive\n"}],"execution_count":3},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ntitanic = pd.read_csv(\"Titanic.csv\", sep = \",\")","metadata":{"id":"RUk74NsUGz0R","cell_id":"ed4a620e64144d8897ef59c375ee2a1b","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":999,"user_tz":240,"timestamp":1650987496358},"deepnote_cell_type":"code"},"outputs":[],"execution_count":4},{"cell_type":"code","source":"titanic","metadata":{"id":"aa0QLHSKJUhY","colab":{"height":424,"base_uri":"https://localhost:8080/"},"cell_id":"ead12a2c96dc49dcbaabf99fbc32c36d","outputId":"b957361e-26eb-472b-8815-e80dfa37c58d","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":15,"user_tz":240,"timestamp":1650987499662},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"     Survived  Pclass  Sex   Age  SibSp  Parch\n0           0       3    0  22.0      1      0\n1           1       1    1  38.0      1      0\n2           1       3    1  26.0      0      0\n3           1       1    1  35.0      1      0\n4           0       3    0  35.0      0      0\n..        ...     ...  ...   ...    ...    ...\n709         0       3    1  39.0      0      5\n710         0       2    0  27.0      0      0\n711         1       1    1  19.0      0      0\n712         1       1    0  26.0      0      0\n713         0       3    0  32.0      0      0\n\n[714 rows x 6 columns]","text/html":"\n  <div id=\"df-324663aa-134b-4b33-b69c-daf20a689507\">\n    <div class=\"colab-df-container\">\n      <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>Survived</th>\n      <th>Pclass</th>\n      <th>Sex</th>\n      <th>Age</th>\n      <th>SibSp</th>\n      <th>Parch</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>22.0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>38.0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>26.0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>35.0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>35.0</td>\n      <td>0</td>\n      <td>0</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    </tr>\n    <tr>\n      <th>709</th>\n      <td>0</td>\n      <td>3</td>\n      <td>1</td>\n      <td>39.0</td>\n      <td>0</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>710</th>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>27.0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>711</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>19.0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>712</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>26.0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>713</th>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>32.0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>714 rows × 6 columns</p>\n</div>\n      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-324663aa-134b-4b33-b69c-daf20a689507')\"\n              title=\"Convert this dataframe to an interactive table.\"\n              style=\"display:none;\">\n        \n  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n       width=\"24px\">\n    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n  </svg>\n      </button>\n      \n  <style>\n    .colab-df-container {\n      display:flex;\n      flex-wrap:wrap;\n      gap: 12px;\n    }\n\n    .colab-df-convert {\n      background-color: #E8F0FE;\n      border: none;\n      border-radius: 50%;\n      cursor: pointer;\n      display: none;\n      fill: #1967D2;\n      height: 32px;\n      padding: 0 0 0 0;\n      width: 32px;\n    }\n\n    .colab-df-convert:hover {\n      background-color: #E2EBFA;\n      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n      fill: #174EA6;\n    }\n\n    [theme=dark] .colab-df-convert {\n      background-color: #3B4455;\n      fill: #D2E3FC;\n    }\n\n    [theme=dark] .colab-df-convert:hover {\n      background-color: #434B5C;\n      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n      fill: #FFFFFF;\n    }\n  </style>\n\n      <script>\n        const buttonEl =\n          document.querySelector('#df-324663aa-134b-4b33-b69c-daf20a689507 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-324663aa-134b-4b33-b69c-daf20a689507');\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          dataTable['output_type'] = 'display_data';\n          await google.colab.output.renderOutput(dataTable, element);\n          const docLink = document.createElement('div');\n          docLink.innerHTML = docLinkHtml;\n          element.appendChild(docLink);\n        }\n      </script>\n    </div>\n  </div>\n  "},"metadata":{},"execution_count":5}],"execution_count":5},{"cell_type":"code","source":"#Separación en Train y Test\nX = titanic.drop(\"Survived\", axis=1)\ny = titanic.Survived","metadata":{"id":"ePmVvBKMGz0a","cell_id":"a9d1440401a34ade93069db9eeffcb8c","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":581,"user_tz":240,"timestamp":1650987502449},"deepnote_cell_type":"code"},"outputs":[],"execution_count":6},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split \n#Train y Test Split\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42) ","metadata":{"id":"H65UlIGqGz0b","cell_id":"25a2c65d5c9d4ddcaeef27d87d99500f","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":10,"user_tz":240,"timestamp":1650987504514},"deepnote_cell_type":"code"},"outputs":[],"execution_count":7},{"cell_type":"code","source":"#Arbol de Decision\nfrom sklearn.tree import DecisionTreeClassifier \narbol_de_decision = DecisionTreeClassifier(max_depth=4, random_state = 42) ","metadata":{"id":"dMkkeLr_Gz0b","cell_id":"e168edb531f34cf2a14f3b5e53d4bac5","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":820,"user_tz":240,"timestamp":1650987506965},"deepnote_cell_type":"code"},"outputs":[],"execution_count":8},{"cell_type":"code","source":"#Fit\narbol_de_decision.fit(X_train,y_train) #Entrenamos el modelo","metadata":{"id":"e1a41zxHGz0c","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"3fd54181d20e40dfb674a638e1b1fc83","outputId":"65d760c7-c9c3-431d-9a41-c4c8d25bb0fe","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":12,"user_tz":240,"timestamp":1650987508830},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"DecisionTreeClassifier(max_depth=4, random_state=42)"},"metadata":{},"execution_count":9}],"execution_count":9},{"cell_type":"code","source":"#Prediccion\ny_test_pred = arbol_de_decision.predict(X_test)","metadata":{"id":"onRr32ePGz0e","cell_id":"ff3b2dab9460487fb820667643d106cb","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":518,"user_tz":240,"timestamp":1650987509932},"deepnote_cell_type":"code"},"outputs":[],"execution_count":10},{"cell_type":"code","source":"y_test_pred","metadata":{"id":"gPhWg8e4JZC4","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"6197a55a74d9486c9b73d68c3907964c","outputId":"9ae376d6-a610-4d02-8dcc-2b4514236026","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":7,"user_tz":240,"timestamp":1650987511067},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"array([0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1,\n       1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n       0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0,\n       0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0,\n       0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1,\n       0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0,\n       0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1,\n       0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1,\n       0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0,\n       1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0])"},"metadata":{},"execution_count":11}],"execution_count":11},{"cell_type":"code","source":"y_test","metadata":{"id":"vA2kBlp3N0ph","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"149b9f7080dc4e74b65a07edde863348","outputId":"d57715d8-3a41-42b0-b06e-063f58bbaf28","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":7,"user_tz":240,"timestamp":1650987512702},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"120    0\n329    1\n39     1\n294    1\n654    0\n      ..\n534    0\n393    0\n382    1\n223    0\n140    0\nName: Survived, Length: 215, dtype: int64"},"metadata":{},"execution_count":12}],"execution_count":12},{"cell_type":"markdown","source":"A lo largo de este notebook, se solicita calcular las métricas requeridas como así también su correspondiente interpretación: \n\n1. Calcular la métrica Accuracy.","metadata":{"id":"CP2yDeswGz0f","cell_id":"0b261739683b4516b77dc7a7e7cc0991","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"###Completar\nfrom sklearn.metrics import accuracy_score\naccuracy_score(y_test,y_test_pred)","metadata":{"id":"969UJrXFGz0g","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"1c2bec13f02841289c02978cbeffeca8","outputId":"488ce2c8-e3a6-4030-826a-16e1a583e62e","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":9,"user_tz":240,"timestamp":1650987514658},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"0.7674418604651163"},"metadata":{},"execution_count":13}],"execution_count":13},{"cell_type":"markdown","source":"2. Crear la Matriz de Confusión","metadata":{"id":"gwk3P7EHGz0h","cell_id":"8be65b5156424b2f953d255baba303de","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"###Completar\nfrom sklearn.metrics import confusion_matrix\nconfusion_matrix(y_test, y_test_pred) ","metadata":{"id":"0ctBrsLnGz0i","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"2af2aa159fb1444f94460e19ddd52a0c","outputId":"58c702a9-3d2f-4f12-c711-e8f99c13fab5","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":6,"user_tz":240,"timestamp":1650987516085},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"array([[102,  24],\n       [ 26,  63]])"},"metadata":{},"execution_count":14}],"execution_count":14},{"cell_type":"markdown","source":"3. Calcular la métrica Precision","metadata":{"id":"BCLRDh6JGz0i","cell_id":"44028f18679f462bbf3535d16330cd1e","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"###Completar\nfrom sklearn.metrics import precision_score\nprecision_score(y_test, y_test_pred) ","metadata":{"id":"QZ6tkCBHGz0j","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"a2800f8d151446bca4e054a7d34356fc","outputId":"9872264c-de30-4cf0-a5c1-12e0f2c1157f","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":12,"user_tz":240,"timestamp":1650987517996},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"0.7241379310344828"},"metadata":{},"execution_count":15}],"execution_count":15},{"cell_type":"markdown","source":"4. Calcular la métrica Recall","metadata":{"id":"rGI3BdhBGz0k","cell_id":"2bae70c4340949df8c57686939dd4570","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"###Completar\nfrom sklearn.metrics import recall_score\nrecall_score(y_test, y_test_pred) ","metadata":{"id":"JjgabsSGGz0k","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"aa4c92d432564e7f9defc41f1b551a8c","outputId":"2dad1d08-dd31-4801-fd56-eabf30dc8c6b","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":703,"user_tz":240,"timestamp":1650987519335},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"0.7078651685393258"},"metadata":{},"execution_count":16}],"execution_count":16},{"cell_type":"markdown","source":"5. Calcular la métrica F1 score","metadata":{"id":"A43J4B04Gz0k","cell_id":"162b187b05554d11b2edc999f455e574","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"###Completar\nfrom sklearn.metrics import f1_score\nf1_score(y_test, y_test_pred) ","metadata":{"id":"U0dTmEfSGz0l","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"02fe8b9ead0e4ef8b86bac654333ca4f","outputId":"2ba84bf6-d760-4a66-c0ac-dc383f9ac2e5","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":717,"user_tz":240,"timestamp":1650987520682},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"0.7159090909090908"},"metadata":{},"execution_count":17}],"execution_count":17},{"cell_type":"markdown","source":"6. classification_report","metadata":{"id":"3_mrp9N0PXcV","cell_id":"c36ca181bc8e47d38b1b4822f4539e18","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"from sklearn.metrics import classification_report\nprint(classification_report(y_test,y_test_pred))","metadata":{"id":"MIcqxQRiO9ow","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"e7829cc248844a5587caba4adca20fea","outputId":"d83faea3-d135-4d03-e9ce-672a0d0330f6","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":6,"user_tz":240,"timestamp":1650987521821},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stdout","text":"              precision    recall  f1-score   support\n\n           0       0.80      0.81      0.80       126\n           1       0.72      0.71      0.72        89\n\n    accuracy                           0.77       215\n   macro avg       0.76      0.76      0.76       215\nweighted avg       0.77      0.77      0.77       215\n\n"}],"execution_count":18},{"cell_type":"markdown","source":"6. Calcular y plotear la Curva ROC","metadata":{"id":"ML5ByCWtGz0l","cell_id":"5ebf9eae8e2f4d32ba3cb91c9e62c0e2","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"###Completar\nfrom sklearn.metrics import roc_curve, roc_auc_score\nimport matplotlib.pyplot as plt\n\ny_score1 = arbol_de_decision.predict_proba(X_test)[:,1]\ny_score1","metadata":{"id":"f8zj93bxGz0l","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"10d2ba40bb384f6592b3563f400f3f5c","outputId":"d0b02fe6-3dea-4ae4-b988-4239f9c1ed54","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":4,"user_tz":240,"timestamp":1650987523826},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"array([0.03797468, 1.        , 0.91666667, 0.98360656, 0.03797468,\n       0.1474359 , 0.15789474, 0.91666667, 0.15789474, 0.66666667,\n       0.98360656, 0.91666667, 0.54285714, 0.03797468, 0.98360656,\n       0.1474359 , 0.1474359 , 0.98360656, 0.1474359 , 0.98360656,\n       0.16666667, 0.91666667, 0.66666667, 0.54285714, 0.1474359 ,\n       0.1474359 , 0.1474359 , 0.1474359 , 0.66666667, 0.03797468,\n       0.66666667, 0.66666667, 0.54285714, 0.03797468, 0.03797468,\n       0.15789474, 0.03797468, 0.21621622, 0.21621622, 0.03797468,\n       0.1474359 , 0.1474359 , 0.98360656, 0.03797468, 0.21621622,\n       0.91666667, 0.91666667, 0.66666667, 0.54285714, 0.98360656,\n       0.54285714, 0.91666667, 0.54285714, 0.1474359 , 0.1474359 ,\n       0.91666667, 0.54285714, 0.        , 0.1474359 , 0.03797468,\n       0.98360656, 0.1474359 , 0.91666667, 0.1474359 , 0.1474359 ,\n       0.21621622, 0.1474359 , 0.98360656, 0.1474359 , 0.54285714,\n       0.1474359 , 0.98360656, 0.1474359 , 0.1474359 , 0.03797468,\n       0.1474359 , 0.1474359 , 0.1474359 , 0.15789474, 0.54285714,\n       0.16666667, 0.54285714, 0.15789474, 0.1474359 , 0.1474359 ,\n       0.66666667, 0.16666667, 0.03797468, 0.1474359 , 0.1474359 ,\n       0.21621622, 0.98360656, 0.91666667, 0.1474359 , 0.91666667,\n       0.66666667, 0.98360656, 0.54285714, 0.1474359 , 0.15789474,\n       0.91666667, 0.91666667, 0.1474359 , 0.16666667, 0.91666667,\n       0.03797468, 0.1474359 , 0.54285714, 0.03797468, 0.91666667,\n       0.1474359 , 0.1474359 , 0.1474359 , 0.21621622, 0.1474359 ,\n       0.15789474, 0.1474359 , 0.66666667, 0.1474359 , 0.03797468,\n       0.98360656, 0.1474359 , 0.1474359 , 0.1474359 , 0.98360656,\n       0.1474359 , 0.1474359 , 0.91666667, 0.1474359 , 0.98360656,\n       0.1474359 , 0.1474359 , 0.1474359 , 0.91666667, 1.        ,\n       0.1474359 , 0.03797468, 0.54285714, 0.1474359 , 0.        ,\n       0.03797468, 0.91666667, 0.98360656, 0.15789474, 0.1474359 ,\n       0.1474359 , 0.03797468, 0.1474359 , 0.54285714, 0.1474359 ,\n       0.03797468, 0.15789474, 0.1474359 , 0.66666667, 0.1474359 ,\n       0.1474359 , 0.16666667, 0.1474359 , 0.91666667, 0.21621622,\n       0.1474359 , 0.1474359 , 0.1474359 , 0.98360656, 0.03797468,\n       0.91666667, 0.98360656, 0.1474359 , 0.98360656, 0.1474359 ,\n       0.21621622, 0.91666667, 0.66666667, 0.91666667, 0.54285714,\n       0.91666667, 0.03797468, 0.1474359 , 0.03797468, 0.03797468,\n       0.66666667, 0.15789474, 0.98360656, 0.54285714, 0.1474359 ,\n       0.1474359 , 0.98360656, 0.54285714, 0.91666667, 0.03797468,\n       0.1474359 , 0.66666667, 0.91666667, 0.66666667, 0.66666667,\n       0.54285714, 0.1474359 , 0.03797468, 0.98360656, 0.15789474,\n       0.1474359 , 0.1474359 , 0.1474359 , 1.        , 0.1474359 ,\n       0.1474359 , 0.66666667, 0.66666667, 0.03797468, 0.98360656,\n       0.1474359 , 0.21621622, 0.66666667, 0.1474359 , 0.21621622])"},"metadata":{},"execution_count":19}],"execution_count":19},{"cell_type":"code","source":"# Calculo de tasas \nfalse_positive_rate1, true_positive_rate1, threshold1 = roc_curve(y_test, y_score1)","metadata":{"id":"NHBqRANrZCxr","cell_id":"5e161a570bbe4c10a1ff4bb8e6f96034","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":2,"user_tz":240,"timestamp":1650987526424},"deepnote_cell_type":"code"},"outputs":[],"execution_count":20},{"cell_type":"code","source":"  print('roc_auc_score for DecisionTree: ', roc_auc_score(y_test, y_score1))","metadata":{"id":"xtVWeq7MZIl7","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"396bdfb36f874a5686a406069118310e","outputId":"07e0b388-65c2-4dd5-e8f8-4095ac62171e","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":6,"user_tz":240,"timestamp":1650987528469},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stdout","text":"roc_auc_score for DecisionTree:  0.8243267344390939\n"}],"execution_count":21},{"cell_type":"code","source":"plt.subplots(1, figsize=(8,6))\nplt.title('Receiver Operating Characteristic - DecisionTree')\nplt.plot(false_positive_rate1, true_positive_rate1)\nplt.plot([0, 1], ls=\"--\")\nplt.plot([0, 0], [1, 0] , c=\".7\"), plt.plot([1, 1] , c=\".7\")\nplt.ylabel('True Positive Rate')\nplt.xlabel('False Positive Rate')\nplt.show()","metadata":{"id":"bRZiJYstZOAG","colab":{"height":404,"base_uri":"https://localhost:8080/"},"cell_id":"a7c6f056489b4104b60900b39cce1652","outputId":"424b709a-9ac6-4052-ab04-183d63bf7632","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":520,"user_tz":240,"timestamp":1650987530930},"deepnote_cell_type":"code"},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 576x432 with 1 Axes>","image/png":"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