class sklearn.linear_model.MultiTaskLassoCV(eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, normalize=False, max_iter=1000

Warning DEPRECATED class

class sklearn.model_selection.LeavePOut(p)

Score, and cross-validated scores As we have seen, every estimator exposes a score method that can judge the

sklearn.metrics.pairwise.manhattan_distances(X, Y=None, sum_over_features=True, size_threshold=500000000.0)

Stochastic Gradient Descent (SGD) is a simple yet very efficient approach to discriminative learning of linear classifiers under convex loss functions

When performing classification you often want to predict not only the class label, but also the associated probability. This probability gives you some

class sklearn.preprocessing.MinMaxScaler(feature_range=(0, 1), copy=True)

sklearn.svm.l1_min_c(X, y, loss='squared_hinge', fit_intercept=True, intercept_scaling=1.0)

class sklearn.gaussian_process.GaussianProcessRegressor(kernel=None, alpha=1e-10, optimizer='fmin_l_bfgs_b', n_restarts_optimizer=0