There are 3 different approaches to evaluate the quality of predictions of a model: Estimator score

sklearn.preprocessing.scale(X, axis=0, with_mean=True, with_std=True, copy=True)

The problem solved in supervised learning

sklearn.metrics.f1_score(y_true, y_pred, labels=None, pos_label=1, average='binary', sample_weight=None)

sklearn.metrics.silhouette_score(X, labels, metric='euclidean', sample_size=None, random_state=None, **kwds)

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

class sklearn.feature_selection.RFE(estimator, n_features_to_select=None, step=1, verbose=0)

class sklearn.exceptions.NonBLASDotWarning

class sklearn.ensemble.IsolationForest(n_estimators=100, max_samples='auto', contamination=0.1, max_features=1.0, bootstrap=False

Plot the confidence ellipsoids of each class and decision boundary print(__doc__)