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

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)

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