This is an example of applying Non-negative Matrix Factorization and Latent Dirichlet Allocation on a

class sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis(priors=None, reg_param=0.0, store_covariances=False

sklearn.linear_model.lasso_path(X, y, eps=0.001, n_alphas=100, alphas=None, precompute='auto', Xy=None, copy_X=True, coef_init=None

sklearn.model_selection.train_test_split(*arrays, **options)

class sklearn.decomposition.MiniBatchSparsePCA(n_components=None, alpha=1, ridge_alpha=0.01, n_iter=100, callback=None, batch_size=3

A plot that compares the various convex loss functions supported by

sklearn.ensemble.partial_dependence.partial_dependence(gbrt, target_variables, grid=None, X=None, percentiles=(0

This is an example showing how scikit-learn can be used for classification using an out-of-core approach: learning from data that doesn?t fit into

class sklearn.neighbors.NearestCentroid(metric='euclidean', shrink_threshold=None)

Plot decision function of a weighted dataset, where the size of points is proportional to its weight. The sample weighting rescales the C parameter, which means