Shows how shrinkage improves classification.

sklearn.tree.export_graphviz()

class sklearn.model_selection.PredefinedSplit(test_fold)

When working with covariance estimation, the usual approach is to use a maximum likelihood estimator, such as the

class sklearn.linear_model.MultiTaskElasticNet(alpha=1.0, l1_ratio=0.5, fit_intercept=True, normalize=False, copy_X=True,

sklearn.metrics.pairwise.additive_chi2_kernel(X, Y=None)

Fit Ridge and HuberRegressor on a dataset with outliers. The example shows that the predictions in ridge are strongly influenced

This example uses

sklearn.metrics.completeness_score(labels_true, labels_pred)

Plot the class probabilities of the first sample in a toy dataset predicted by three different classifiers and averaged by the