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.

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

class sklearn.decomposition.ProjectedGradientNMF(*args, **kwargs)

sklearn.svm.libsvm.fit() Train the model using libsvm (low-level method)

class sklearn.ensemble.RandomForestClassifier(n_estimators=10, criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=1

sklearn.metrics.pairwise.pairwise_kernels(X, Y=None, metric='linear', filter_params=False, n_jobs=1, **kwds)

class sklearn.feature_selection.GenericUnivariateSelect(score_func=, mode='percentile', param=1e-05)

Naive Bayes methods are a set of supervised learning algorithms based on applying Bayes? theorem with the ?naive? assumption of independence between every pair of features. Given

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