class sklearn.mixture.GaussianMixture(n_components=1, covariance_type='full', tol=0.001, reg_covar=1e-06, max_iter=100, n_init=1,

class sklearn.kernel_ridge.KernelRidge(alpha=1, kernel='linear', gamma=None, degree=3, coef0=1, kernel_params=None)

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

sklearn.metrics.pairwise.linear_kernel(X, Y=None)

Biclustering can be performed with the module

This example plots the ellipsoids obtained from a toy dataset (mixture of three Gaussians) fitted by the Baye

This is an example of applying Non-negative Matrix Factorization and Latent Dirichlet Allocation on a

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

sklearn.cluster.k_means(X, n_clusters, init='k-means++', precompute_distances='auto', n_init=10, max_iter=300, verbose=False, tol=0.0001

class sklearn.linear_model.HuberRegressor(epsilon=1.35, max_iter=100, alpha=0.0001, warm_start=False, fit_intercept=True, tol=1e-05)