Simple usage of various cross decomposition algorithms: - PLSCanonical - PLSRegression, with multivariate response, a.k.a. PLS2 - PLSRegression, with univariate

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

This example shows that imputing the missing values can give better results than discarding the samples containing any missing value. Imputing

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

sklearn.feature_extraction.image.reconstruct_from_patches_2d(patches, image_size)

class sklearn.ensemble.IsolationForest(n_estimators=100, max_samples='auto', contamination=0.1, max_features=1.0, bootstrap=False

sklearn.metrics.get_scorer(scoring)

sklearn.metrics.pairwise.kernel_metrics()

class sklearn.ensemble.VotingClassifier(estimators, voting='hard', weights=None, n_jobs=1)

Warning DEPRECATED