sklearn.datasets.fetch_lfw_pairs(subset='train', data_home=None, funneled=True, resize=0.5, color=False, slice_=(slice(70, 195

The dataset used in this example is a preprocessed excerpt of the ?Labeled Faces in the Wild?, aka

Finds core samples of high density and expands clusters from them. print(__doc__) import numpy as np from

sklearn.utils.estimator_checks.check_estimator(Estimator)

class sklearn.linear_model.RandomizedLasso(alpha='aic', scaling=0.5, sample_fraction=0.75, n_resampling=200, selection_threshold=0

class sklearn.pipeline.FeatureUnion(transformer_list, n_jobs=1, transformer_weights=None)

An example comparing the effect of reconstructing noisy fragments of a raccoon face image using firstly online

sklearn.model_selection.fit_grid_point(X, y, estimator, parameters, train, test, scorer, verbose, error_score='raise', **fit_params)

This is an example showing the prediction latency of various scikit-learn estimators. The goal is to measure the latency one can expect when doing predictions either

class sklearn.linear_model.LassoLarsCV(fit_intercept=True, verbose=False, max_iter=500, normalize=True, precompute='auto', cv=None