37ceb892665f8643a2ce19d64569cabc8cc2b86d,bugbug/model.py,Model,train,#Model#,32

Before Change

        y_pred = self.clf.predict(X_test)

        print(metrics.confusion_matrix(y_test, y_pred, labels=[1, 0]))
        print(classification_report_imbalanced(y_test, y_pred, labels=[1, 0]))

        // Evaluate results on the test set for some confidence thresholds.
        for confidence_threshold in [0.6, 0.7, 0.8, 0.9]:

After Change

                y_test_filter.append(y_test[i])
                y_pred_filter.append(argmax)

            y_pred_filter = self.clf._le.inverse_transform(y_pred_filter)

            print("\nConfidence threshold > {} - {} classified".format(confidence_threshold, len(y_test_filter)))
            print(metrics.confusion_matrix(y_test_filter, y_pred_filter, labels=class_names))
            print(classification_report_imbalanced(y_test_filter, y_pred_filter, labels=class_names))

Instances