Plot decision function of a weighted dataset, where the size of points is proportional to its weight. The sample weighting rescales the C parameter, which means

This example uses a large dataset of faces to learn a set of 20 x 20 images patches that constitute faces. From the programming standpoint

Comparison for decision boundary generated on iris dataset between Label Propagation and SVM. This demonstrates

Using the GraphLasso estimator to learn a covariance and sparse precision from a small number of samples. To estimate a probabilistic model (e.g

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

Simple usage of Pipeline that runs successively a univariate feature selection with anova and then a C-SVM of the selected features.

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

Compares FeatureHasher and DictVectorizer by using both to vectorize text documents. The example demonstrates syntax and speed only; it doesn

This example compares 2 dimensionality reduction strategies: univariate feature selection with Anova feature

Illustration of how the performance of an estimator on unseen data (test data) is not the same as the performance on training data. As the regularization increases