linear_models.py 文件源码

def regression():
    # Generate a random regression problem
    X, y = make_regression(n_samples=10000, n_features=100,
                           n_informative=75, n_targets=1, noise=0.05,
                           random_state=1111, bias=0.5)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25,
                                                        random_state=1111)

    model = LinearRegression(lr=0.01, max_iters=2000, penalty='l2', C=0.03)
    model.fit(X_train, y_train)
    predictions = model.predict(X_test)
    print('regression mse', mean_squared_error(y_test, predictions))