def _test_ridge_classifiers(filter_): for clf in (RidgeClassifier(), RidgeClassifierCV()): clf.fit(filter_(X_iris), y_iris) y_pred = clf.predict(filter_(X_iris)) assert np.mean(y_iris == y_pred) >= 0.8 clf = RidgeClassifierCV() n_samples = X_iris.shape[0] cv = KFold(n_samples, 5) clf.fit(filter_(X_iris), y_iris, cv=cv) y_pred = clf.predict(filter_(X_iris)) assert np.mean(y_iris == y_pred) >= 0.8
def _test_ridge_classifiers(filter_): n_classes = np.unique(y_iris).shape[0] n_features = X_iris.shape[1] for clf in (RidgeClassifier(), RidgeClassifierCV()): clf.fit(filter_(X_iris), y_iris) assert_equal(clf.coef_.shape, (n_classes, n_features)) y_pred = clf.predict(filter_(X_iris)) assert np.mean(y_iris == y_pred) >= 0.8 clf = RidgeClassifierCV() n_samples = X_iris.shape[0] cv = KFold(n_samples, 5) clf.fit(filter_(X_iris), y_iris, cv=cv) y_pred = clf.predict(filter_(X_iris)) assert np.mean(y_iris == y_pred) >= 0.8