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updated ml1/2_6: using scorer to avoid traning warnings
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@ -416,7 +416,7 @@
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"source": [
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"# Set the parameters by cross-validation\n",
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"\n",
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"from sklearn.metrics import classification_report\n",
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"from sklearn.metrics import classification_report, recall_score, precision_score, make_scorer\n",
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"\n",
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"# set of parameters to test\n",
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"tuned_parameters = [{'max_depth': np.arange(3, 10),\n",
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@ -434,8 +434,13 @@
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" print(\"# Tuning hyper-parameters for %s\" % score)\n",
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" print()\n",
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"\n",
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" if score == 'precision':\n",
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" scorer = make_scorer(precision_score, average='weighted', zero_division=0)\n",
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" elif score == 'recall':\n",
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" scorer = make_scorer(recall_score, average='weighted', zero_division=0)\n",
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" \n",
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" # cv = the fold of the cross-validation cv, defaulted to 5\n",
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" gs = GridSearchCV(DecisionTreeClassifier(), tuned_parameters, cv=10, scoring='%s_weighted' % score)\n",
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" gs = GridSearchCV(DecisionTreeClassifier(), tuned_parameters, cv=10, scoring=scorer)\n",
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" gs.fit(x_train, y_train)\n",
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"\n",
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" print(\"Best parameters set found on development set:\")\n",
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@ -552,7 +557,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.7"
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"version": "3.8.6"
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},
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"latex_envs": {
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"LaTeX_envs_menu_present": true,
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