class sklearn.linear_model.Ridge(alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, tol=0.001, solver='auto',

Learning the parameters of a prediction function and testing it on the same data is a methodological mistake: a model that would just repeat

class sklearn.linear_model.OrthogonalMatchingPursuitCV(copy=True, fit_intercept=True, normalize=True, max_iter=None

An example showing how the scikit-learn can be used to recognize images of hand-written digits. This example is commented in the

Kernel ridge regression (KRR) [M2012]

sklearn.metrics.matthews_corrcoef(y_true, y_pred, sample_weight=None)

class sklearn.svm.OneClassSVM(kernel='rbf', degree=3, gamma='auto', coef0=0.0, tol=0.001, nu=0.5, shrinking=True, cache_size=200, verbose=False

class sklearn.multiclass.OneVsOneClassifier(estimator, n_jobs=1)

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

class sklearn.linear_model.ElasticNetCV(l1_ratio=0.5, eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, normalize=False,