Both kernel ridge regression (KRR) and Gaussian process regression (GPR) learn a target function by employing internally the ?kernel

class sklearn.linear_model.RANSACRegressor(base_estimator=None, min_samples=None, residual_threshold=None, is_data_valid=None

sklearn.metrics.auc(x, y, reorder=False)

A tutorial exercise regarding the use of classification techniques on the Digits dataset. This exercise is used in the

This example illustrates and compares the bias-variance decomposition of the expected mean squared error of a single estimator against

class sklearn.base.RegressorMixin

sklearn.metrics.pairwise.paired_distances(X, Y, metric='euclidean', **kwds)

sklearn.datasets.fetch_rcv1(data_home=None, subset='all', download_if_missing=True, random_state=None, shuffle=False)

sklearn.datasets.fetch_20newsgroups_vectorized(subset='train', remove=(), data_home=None)

Computes path on IRIS dataset. print(__doc__) # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>