sklearn.metrics.average_precision_score(y_true, y_score, average='macro', sample_weight=None)

sklearn.datasets.load_breast_cancer(return_X_y=False)

Warning This implementation is not intended for large-scale applications

class sklearn.feature_selection.RFECV(estimator, step=1, cv=None, scoring=None, verbose=0, n_jobs=1)

class sklearn.gaussian_process.kernels.ConstantKernel(constant_value=1.0, constant_value_bounds=(1e-05, 100000.0))

class sklearn.multioutput.MultiOutputClassifier(estimator, n_jobs=1)

class sklearn.gaussian_process.kernels.CompoundKernel(kernels)

sklearn.feature_selection.f_regression(X, y, center=True)

class sklearn.neural_network.BernoulliRBM(n_components=256, learning_rate=0.1, batch_size=10, n_iter=10, verbose=0, random_state=None)

This example illustrates how sigmoid calibration changes predicted probabilities for a 3-class classification problem. Illustrated is the