class sklearn.linear_model.LogisticRegression(penalty='l2', dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1

class sklearn.linear_model.ARDRegression(n_iter=300, tol=0.001, alpha_1=1e-06, alpha_2=1e-06, lambda_1=1e-06, lambda_2=1e-06, compute_score=False

sklearn.mixture is a package which enables one to learn Gaussian Mixture Models (diagonal, spherical, tied and full covariance matrices supported), sample

A two-dimensional classification example showing iso-probability lines for the predicted probabilities.

Demonstrate Gradient Boosting on the Boston housing dataset. This example fits a Gradient Boosting model with least squares loss and 500 regression trees

Warning DEPRECATED

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

sklearn.metrics.precision_recall_curve(y_true, probas_pred, pos_label=None, sample_weight=None)

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

sklearn.metrics.jaccard_similarity_score(y_true, y_pred, normalize=True, sample_weight=None)