Created
February 21, 2021 19:16
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Test RandomForest performs better with same depth
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| def test_rf_better_than_dt(dummy_titanic): | |
| X_train, y_train, X_test, y_test = dummy_titanic | |
| dt = DecisionTree(depth_limit=10) | |
| dt.fit(X_train, y_train) | |
| rf = RandomForest(depth_limit=10, num_trees=7, col_subsampling=0.8, row_subsampling=0.8) | |
| rf.fit(X_train, y_train) | |
| pred_test_dt = dt.predict(X_test) | |
| pred_test_binary_dt = np.round(pred_test_dt) | |
| acc_test_dt = accuracy_score(y_test, pred_test_binary_dt) | |
| auc_test_dt = roc_auc_score(y_test, pred_test_dt) | |
| pred_test_rf = rf.predict(X_test) | |
| pred_test_binary_rf = np.round(pred_test_rf) | |
| acc_test_rf = accuracy_score(y_test, pred_test_binary_rf) | |
| auc_test_rf = roc_auc_score(y_test, pred_test_rf) | |
| assert acc_test_rf > acc_test_dt, 'RandomForest should have higher accuracy than DecisionTree on test set.' | |
| assert auc_test_rf > auc_test_dt, 'RandomForest should have higher AUC ROC than DecisionTree on test set.' |
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