// store the residuals for each model
m1_residuals = []
m2_residuals = []
for train_window_indices, val_index in cv.split(train):
tr_fold = train[train_window_indices]
model1.fit(tr_fold)
After Change
// Pick based on which has a lower mean error rate
m1_average_error = np.average(model1_cv_scores)
m2_average_error = np.average(model2_cv_scores)
errors = [m1_average_error, m2_average_error]
models = [model1, model2]
// print out the answer
