3.6.10.6. Use the RidgeCV and LassoCV to set the regularization parameter

Load the diabetes dataset

from sklearn.datasets import load_diabetes
data = load_diabetes()
X, y = data.data, data.target
print(X.shape)

Out:

(442, 10)

Compute the cross-validation score with the default hyper-parameters

from sklearn.model_selection import cross_val_score
from sklearn.linear_model import Ridge, Lasso

for Model in [Ridge, Lasso]:
    model = Model()
    print('%s: %s' % (Model.__name__,
                      cross_val_score(model, X, y).mean()))

Out:

Ridge: 0.4101758336587286
Lasso: 0.3375597834274947

We compute the cross-validation score as a function of alpha, the strength of the regularization for Lasso and Ridge

import numpy as np
from matplotlib import pyplot as plt

alphas = np.logspace(-3, -1, 30)

plt.figure(figsize=(5, 3))

for Model in [Lasso, Ridge]:
    scores = [cross_val_score(Model(alpha), X, y, cv=3).mean()
            for alpha in alphas]
    plt.plot(alphas, scores, label=Model.__name__)

plt.legend(loc='lower left')
plt.xlabel('alpha')
plt.ylabel('cross validation score')
plt.tight_layout()
plt.show()

Total running time of the script: ( 0 minutes 0.355 seconds)