sklearn.datasets.load_sample_image(image_name)

sklearn.pipeline.make_union(*transformers)

sklearn.utils.shuffle(*arrays, **options)

class sklearn.model_selection.ParameterSampler(param_distributions, n_iter, random_state=None)

Estimates Lasso and Elastic-Net regression models on a manually generated sparse signal corrupted with an additive noise. Estimated coefficients are

class sklearn.feature_extraction.image.PatchExtractor(patch_size=None, max_patches=None, random_state=None)

sklearn.feature_selection.mutual_info_regression(X, y, discrete_features='auto', n_neighbors=3, copy=True, ran

sklearn.metrics.label_ranking_average_precision_score(y_true, y_score)

class sklearn.decomposition.FactorAnalysis(n_components=None, tol=0.01, copy=True, max_iter=1000, noise_variance_init=None, s

sklearn.preprocessing.label_binarize(y, classes, neg_label=0, pos_label=1, sparse_output=False)