Using orthogonal matching pursuit for recovering a sparse signal from a noisy measurement encoded with a dictionary print(__doc__)

class sklearn.preprocessing.LabelEncoder

RandomTreesEmbedding provides a way to map data to a very high-dimensional, sparse representation, which might be beneficial for classification

class sklearn.decomposition.SparseCoder(dictionary, transform_algorithm='omp', transform_n_nonzero_coefs=None, transform_alpha=None

Clustering of unlabeled data can be performed with the module

4.1.1. Pipeline: chaining estimators

This example studies the scalability profile of approximate 10-neighbors queries using the LSHForest with n_estimators=20 and

class sklearn.gaussian_process.kernels.Matern(length_scale=1.0, length_scale_bounds=(1e-05, 100000.0), nu=1.5)

class sklearn.feature_extraction.text.TfidfTransformer(norm=u'l2', use_idf=True, smooth_idf=True, sublinear_tf=False)

This examples shows the use of forests of trees to evaluate the importance of features on an artificial classification task. The red bars are the feature