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

Warning DEPRECATED

class sklearn.linear_model.LogisticRegressionCV(Cs=10, fit_intercept=True, cv=None, dual=False, penalty='l2', scoring=None

Warning All classifiers in scikit-learn do multiclass classification

class sklearn.linear_model.LassoLarsCV(fit_intercept=True, verbose=False, max_iter=500, normalize=True, precompute='auto', cv=None

class sklearn.linear_model.Lars(fit_intercept=True, verbose=False, normalize=True, precompute='auto', n_nonzero_coefs=500, eps=2.2204460492503131e-16

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

class sklearn.calibration.CalibratedClassifierCV(base_estimator=None, method='sigmoid', cv=3)