In this example, an image with connected circles is generated and spectral clustering is used to separate the circles. In these settings, the

Plot the density estimation of a mixture of two Gaussians. Data is generated from two Gaussians with different centers and covariance matrices.

Transform your features into a higher dimensional, sparse space. Then train a linear model on these features. First fit an ensemble of

In many real-world examples, there are many ways to extract features from a dataset. Often it is beneficial to combine several methods to obtain

A tutorial exercise for using different SVM kernels. This exercise is used in the

sklearn.mixture is a package which enables one to learn Gaussian Mixture Models (diagonal, spherical, tied and full covariance matrices supported), sample

sklearn.datasets.make_hastie_10_2(n_samples=12000, random_state=None)

sklearn.metrics.pairwise.cosine_similarity(X, Y=None, dense_output=True)

class sklearn.feature_extraction.text.HashingVectorizer(input=u'content', encoding=u'utf-8', decode_error=u'strict'

class sklearn.feature_extraction.FeatureHasher(n_features=1048576, input_type='dict', dtype=, non_negative=False)