Well calibrated classifiers are probabilistic classifiers for which the output of the predict_proba method can be directly interpreted as a confidence

class sklearn.ensemble.AdaBoostRegressor(base_estimator=None, n_estimators=50, learning_rate=1.0, loss='linear', random_state=None)

sklearn.datasets.make_multilabel_classification(n_samples=100, n_features=20, n_classes=5, n_labels=2, length=50

sklearn.datasets.fetch_lfw_pairs(subset='train', data_home=None, funneled=True, resize=0.5, color=False, slice_=(slice(70, 195

This example demonstrates the Spectral Co-clustering algorithm on the twenty newsgroups dataset. The ?comp.os.ms-windows.misc

sklearn.feature_selection.mutual_info_classif(X, y, discrete_features='auto', n_neighbors=3, copy=True, random_state=None)

class sklearn.model_selection.StratifiedKFold(n_splits=3, shuffle=False, random_state=None)

class sklearn.feature_selection.SelectFpr(score_func=, alpha=0.05)

Plot the maximum margin separating hyperplane within a two-class separable dataset using a Support Vector Machine classifier with linear kernel.

class sklearn.gaussian_process.kernels.Kernel