Plot decision function of a weighted dataset, where the size of points is proportional to its weight.

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

class sklearn.isotonic.IsotonicRegression(y_min=None, y_max=None, increasing=True, out_of_bounds='nan')

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

The

class sklearn.linear_model.LarsCV(fit_intercept=True, verbose=False, max_iter=500, normalize=True, precompute='auto', cv=None, max_n_alphas=1000

class sklearn.gaussian_process.kernels.RationalQuadratic(length_scale=1.0, alpha=1.0, length_scale_bounds=(1e-05

class sklearn.svm.LinearSVR(epsilon=0.0, tol=0.0001, C=1.0, loss='epsilon_insensitive', fit_intercept=True, intercept_scaling=1.0, dual=True

2.9.1. Restricted Boltzmann machines Restricted Boltzmann machines (RBM) are unsupervised nonlinear feature learners based on a probabilistic

Use the Akaike information criterion (AIC), the Bayes Information criterion (BIC) and cross-validation to select an optimal value of the regularization