ridge regression

from sklearn.linear_model import RidgeCV
from sklearn.metrics import mean_squared_error

# root-mean-squared error function
def rmse(ytrue, ypredicted):
    return np.sqrt(mean_squared_error(ytrue, ypredicted))

alphas = [0.005, 0.05, 0.1, 0.3, 1, 3, 5, 10, 15, 30, 80]

ridgeCV = RidgeCV(alphas=alphas, 
                  cv=4).fit(X_train, y_train)

ridgeCV_rmse = rmse(y_test, ridgeCV.predict(X_test))

print("The best alpha is: {} with Root Mean Square Error: {}".format(
                                        ridgeCV.alpha_, ridgeCV_rmse))