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

These images how similar features are merged together using feature agglomeration.

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

When the amount of contamination is known, this example illustrates three different ways of performing

A decision tree is boosted using the AdaBoost.R2 [1] algorithm on a 1D sinusoidal dataset with a small amount of Gaussian noise. 299 boosts (300 decision

When working with covariance estimation, the usual approach is to use a maximum likelihood estimator, such as the

Computes a Theil-Sen Regression on a synthetic dataset. See

Perform binary classification using non-linear SVC with RBF kernel. The target to predict is a XOR of the inputs. The color map illustrates the decision function learned

This example illustrates that GPR with a sum-kernel including a WhiteKernel can estimate the noise level of data. An illustration

This example visualizes some training loss curves for different stochastic learning strategies, including SGD and Adam. Because of time-constraints