This example uses the

class sklearn.gaussian_process.kernels.Product(k1, k2)

class sklearn.gaussian_process.kernels.Exponentiation(kernel, exponent)

sklearn.metrics.pairwise.distance_metrics()

sklearn.datasets.make_s_curve(n_samples=100, noise=0.0, random_state=None)

This is an example showing the prediction latency of various scikit-learn estimators. The goal is to measure the latency one can expect when doing predictions either

Clustering: grouping observations together The problem solved in clustering Given the

class sklearn.model_selection.RandomizedSearchCV(estimator, param_distributions, n_iter=10, scoring=None, fit_params=None

sklearn.metrics.mutual_info_score(labels_true, labels_pred, contingency=None)

class sklearn.random_projection.GaussianRandomProjection(n_components='auto', eps=0.1, random_state=None)