This examples shows the use of forests of trees to evaluate the importance of features on an artificial classification task. The red bars are the feature

class sklearn.preprocessing.MultiLabelBinarizer(classes=None, sparse_output=False)

Kernel ridge regression (KRR) [M2012]

class sklearn.preprocessing.LabelEncoder

sklearn.preprocessing.scale(X, axis=0, with_mean=True, with_std=True, copy=True)

class sklearn.linear_model.SGDRegressor(loss='squared_loss', penalty='l2', alpha=0.0001, l1_ratio=0.15, fit_intercept=True, n_iter=5

class sklearn.model_selection.KFold(n_splits=3, shuffle=False, random_state=None)

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

class sklearn.linear_model.OrthogonalMatchingPursuit(n_nonzero_coefs=None, tol=None, fit_intercept=True, normalize=True

Using orthogonal matching pursuit for recovering a sparse signal from a noisy measurement encoded with a dictionary print(__doc__)