sklearn.linear_model.lars_path(X, y, Xy=None, Gram=None, max_iter=500, alpha_min=0, method='lar', copy_X=True, eps=2.2204460492503131e-16

Computes path on IRIS dataset. print(__doc__) # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>

An example to show covariance estimation with the Mahalanobis distances on Gaussian distributed data. For Gaussian distributed

sklearn.feature_extraction.image.grid_to_graph(n_x, n_y, n_z=1, mask=None, return_as=, dtype=)

sklearn.datasets.make_biclusters(shape, n_clusters, noise=0.0, minval=10, maxval=100, shuffle=True, random_state=None)

Given a small number of observations, we want to recover which features of X are relevant to explain y. For this

This example is meant to illustrate situations where k-means will produce unintuitive and possibly unexpected clusters. In the first three plots, the input

sklearn.datasets.make_friedman3(n_samples=100, noise=0.0, random_state=None)

class sklearn.feature_extraction.text.TfidfVectorizer(input=u'content', encoding=u'utf-8', decode_error=u'strict',

sklearn.metrics.explained_variance_score(y_true, y_pred, sample_weight=None, multioutput='uniform_average')