K-Fold CV comparison

Cross validation is essential but do not forget that the more folds you use, the more computationally expensive cross-validation becomes. In this exercise, you will explore this for yourself. Your job is to perform 3-fold cross-validation and then 10-fold cross-validation on the Gapminder dataset.

In the IPython Shell, you can use %timeit to see how long each 3-fold CV takes compared to 10-fold CV by executing the following cv=3 and cv=10:

%timeit cross_val_score(reg, X, y, cv = ____)

pandas and numpy are available in the workspace as pd and np. The DataFrame has been loaded as df and the feature/target variable arrays X and y have been created.

Import LinearRegression from sklearn.linear_model and cross_val_score from sklearn.model_selection.
Create a linear regression regressor called reg.
Perform 3-fold CV and then 10-fold CV. Compare the resulting mean scores.

Exercise

K-Fold CV comparison

Instructions