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

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

sklearn.cluster.ward_tree(X, connectivity=None, n_clusters=None, return_distance=False)

sklearn.datasets.fetch_covtype(data_home=None, download_if_missing=True, random_state=None, shuffle=False)

class sklearn.model_selection.LeaveOneOut

class sklearn.kernel_approximation.RBFSampler(gamma=1.0, n_components=100, random_state=None)

class sklearn.gaussian_process.kernels.PairwiseKernel(gamma=1.0, gamma_bounds=(1e-05, 100000.0), metric='linear',

Simple usage of Support Vector Machines to classify a sample. It will plot the decision surface and the support vectors.

After training a scikit-learn model, it is desirable to have a way to persist the model for future use without having to retrain. The following section gives you an example

Ridge Regression is the estimator used in this example. Each color in the left plot represents one different dimension of the coefficient vector, and this is displayed as a function of the regularization parameter. The right plot shows how exact the solution is. This example illustrates how a well defined solution is found by Ridge regression and how regularization affects the coefficients and their values. The plot on the right shows how the difference of the coefficients from the estimator c