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Logging training Running DummyClassifier() accuracy: 0.627 average_precision: 0.373 roc_auc: 0.500 recall_macro: 0.500 f1_macro: 0.386 === new best DummyClassifier() (using recall_macro): accuracy: 0.627 average_precision: 0.373 roc_auc: 0.500 recall_macro: 0.500 f1_macro: 0.386 Running GaussianNB() accuracy: 0.924 average_precision: 0.974 roc_auc: 0.984 recall_macro: 0.920 f1_macro: 0.919 === new best GaussianNB() (using recall_macro): accuracy: 0.924 average_precision: 0.974 roc_auc: 0.984 recall_macro: 0.920 f1_macro: 0.919 Running MultinomialNB() accuracy: 0.840 average_precision: 0.914 roc_auc: 0.927 recall_macro: 0.787 f1_macro: 0.807 Running DecisionTreeClassifier(class_weight='balanced', max_depth=1) accuracy: 0.900 average_precision: 0.797 roc_auc: 0.898 recall_macro: 0.898 f1_macro: 0.894 Running DecisionTreeClassifier(class_weight='balanced', max_depth=5) accuracy: 0.940 average_precision: 0.881 roc_auc: 0.938 recall_macro: 0.938 f1_macro: 0.936 === new best DecisionTreeClassifier(class_weight='balanced', max_depth=5) (using recall_macro): accuracy: 0.940 average_precision: 0.881 roc_auc: 0.938 recall_macro: 0.938 f1_macro: 0.936 Running DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01) accuracy: 0.937 average_precision: 0.884 roc_auc: 0.933 recall_macro: 0.936 f1_macro: 0.933 Running LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000) accuracy: 0.979 average_precision: 0.994 roc_auc: 0.995 recall_macro: 0.977 f1_macro: 0.977 === new best LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000) (using recall_macro): accuracy: 0.979 average_precision: 0.994 roc_auc: 0.995 recall_macro: 0.977 f1_macro: 0.977 Running LogisticRegression(class_weight='balanced', max_iter=1000) accuracy: 0.968 average_precision: 0.994 roc_auc: 0.995 recall_macro: 0.967 f1_macro: 0.966 Best model: LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000) Best Scores: accuracy: 0.979 average_precision: 0.994 roc_auc: 0.995 recall_macro: 0.977 f1_macro: 0.977 |