sklearn.metrics.log_loss(y_true, y_pred, eps=1e-15, normalize=True, sample_weight=None, labels=None)

Three different types of SVM-Kernels are displayed below. The polynomial and RBF are especially useful when the data-points are not linearly separable.

Warning DEPRECATED class sklearn

Warning DEPRECATED

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

sklearn.pipeline.make_union(*transformers)

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

sklearn.model_selection.check_cv(cv=3, y=None, classifier=False)

This example illustrates GPC on XOR data. Compared are a stationary, isotropic kernel (RBF) and a non-stationary kernel

class sklearn.gaussian_process.kernels.ExpSineSquared(length_scale=1.0, periodicity=1.0, length_scale_bounds=(1e-05