class sklearn.preprocessing.PolynomialFeatures(degree=2, interaction_only=False, include_bias=True)

class sklearn.model_selection.KFold(n_splits=3, shuffle=False, random_state=None)

Silhouette analysis can be used to study the separation distance between the resulting clusters. The silhouette

sklearn.metrics.homogeneity_completeness_v_measure(labels_true, labels_pred)

sklearn.metrics.pairwise.pairwise_distances(X, Y=None, metric='euclidean', n_jobs=1, **kwds)

A simple graphical frontend for Libsvm mainly intended for didactic purposes. You can create data points by point and click and visualize the decision region induced by different

class sklearn.kernel_approximation.Nystroem(kernel='rbf', gamma=None, coef0=1, degree=3, kernel_params=None, n_components=100

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

class sklearn.model_selection.TimeSeriesSplit(n_splits=3)

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