()
| 43 | |
| 44 | |
| 45 | def regression(): |
| 46 | # Generate a random regression problem |
| 47 | X, y = make_regression( |
| 48 | n_samples=500, |
| 49 | n_features=5, |
| 50 | n_informative=5, |
| 51 | n_targets=1, |
| 52 | noise=0.05, |
| 53 | random_state=1111, |
| 54 | bias=0.5, |
| 55 | ) |
| 56 | X_train, X_test, y_train, y_test = train_test_split( |
| 57 | X, y, test_size=0.1, random_state=1111 |
| 58 | ) |
| 59 | |
| 60 | model = RandomForestRegressor(n_estimators=50, max_depth=10, max_features=3) |
| 61 | model.fit(X_train, y_train) |
| 62 | predictions = model.predict(X_test) |
| 63 | print( |
| 64 | "regression, mse: %s" |
| 65 | % mean_squared_error(y_test.flatten(), predictions.flatten()) |
| 66 | ) |
| 67 | |
| 68 | |
| 69 | if __name__ == "__main__": |
nothing calls this directly
no test coverage detected