bc4a5f5d29bb5a74d9cb254ff4dfed916676c8aa,examples/model_selection/example_cross_validation.py,,,#,16

Before Change

cv = model_selection.SlidingWindowForecastCV(window_size=100, step=24, h=1)

// 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]

After Change

model1_cv_scores = model_selection.cross_val_score(
    model1, train, scoring="mean_squared_error", cv=cv, verbose=2)

model2_cv_scores = model_selection.cross_val_score(
    model2, train, scoring="mean_squared_error", cv=cv, verbose=2)

// 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)

Instances