()
| 15 | |
| 16 | |
| 17 | def regression(): |
| 18 | # Generate a random regression problem |
| 19 | X, y = make_regression( |
| 20 | n_samples=10000, |
| 21 | n_features=100, |
| 22 | n_informative=75, |
| 23 | n_targets=1, |
| 24 | noise=0.05, |
| 25 | random_state=1111, |
| 26 | bias=0.5, |
| 27 | ) |
| 28 | X_train, X_test, y_train, y_test = train_test_split( |
| 29 | X, y, test_size=0.25, random_state=1111 |
| 30 | ) |
| 31 | |
| 32 | model = LinearRegression(lr=0.01, max_iters=2000, penalty="l2", C=0.03) |
| 33 | model.fit(X_train, y_train) |
| 34 | predictions = model.predict(X_test) |
| 35 | print("regression mse", mean_squared_error(y_test, predictions)) |
| 36 | |
| 37 | |
| 38 | def classification(): |
no test coverage detected