sklearn.linear_model.logistic_regression_path(X, y, pos_class=None, Cs=10, fit_intercept=True, max_iter=100, tol=0

class sklearn.neighbors.RadiusNeighborsRegressor(radius=1.0, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski'

An example illustrating the approximation of the feature map of an RBF kernel. It shows how to use

2.5.1. Principal component analysis (PCA) 2.5.1.1. Exact PCA and probabilistic interpretation PCA

class sklearn.kernel_approximation.SkewedChi2Sampler(skewedness=1.0, n_components=100, random_state=None)

sklearn.datasets.fetch_mldata(dataname, target_name='label', data_name='data', transpose_data=True, data_home=None)

sklearn.model_selection.cross_val_predict(estimator, X, y=None, groups=None, cv=None, n_jobs=1, verbose=0, fit_params=None

class sklearn.gaussian_process.kernels.RBF(length_scale=1.0, length_scale_bounds=(1e-05, 100000.0))

sklearn.metrics.pairwise.chi2_kernel(X, Y=None, gamma=1.0)

Warning DEPRECATED