# clf_bNB.fit(X_train, y_train)
clf_mNB.fit(X_train, y_train)
clf_kNN.fit(X_train, y_train)
clf_ridge.fit(X_train, y_train)
clf_SGD.fit(X_train, y_train)
clf_lSVC.fit(X_train, y_train)
clf_SVC.fit(X_train, y_train)
# clf_tree.fit(X_den_train, y_train)
clf_logis.fit(X_den_train, y_train)
# get prediction for this fold run
# pred_bNB = clf_bNB.predict(X_test)
pred_mNB = clf_mNB.predict(X_test)
pred_kNN = clf_kNN.predict(X_test)
pred_ridge = clf_ridge.predict(X_test)
pred_SGD = clf_SGD.predict(X_test)
pred_lSVC = clf_lSVC.predict(X_test)
pred_SVC = clf_SVC.predict(X_test)
# pred_tree = clf_tree.predict(X_den_test)
pred_logis = clf_logis.predict(X_den_test)
# update z array for each model
# z_bNB = np.append(z_bNB , pred_bNB , axis=None)
z_mNB = np.append(z_mNB , pred_mNB , axis=None)
z_kNN = np.append(z_kNN , pred_kNN , axis=None)
z_ridge = np.append(z_ridge , pred_ridge, axis=None)
z_SGD = np.append(z_SGD , pred_SGD , axis=None)
z_lSVC = np.append(z_lSVC , pred_lSVC , axis=None)
z_SVC = np.append(z_SVC , pred_SVC , axis=None)
# z_tree = np.append(z_tree , pred_tree , axis=None)
z_logis = np.append(z_logis , pred_logis, axis=None)
