Branch-AI/one_exercise_per_file/week02/day05/ex01/readme.md

Exercise 1: K-Fold

The goal of this exercise is to learn to use KFold to split the data set in a k-fold cross validation. Most of the time you won't use this function to split your data because this function is used by others as cross_val_score or cross_validate or GridSearchCV ... . But, this allows to understand the splitting and to create a custom one if needed.

X = np.array(np.arange(1,21).reshape(10,-1))
y = np.array(np.arange(1,11))

1. Using KFold, perform a 5-fold cross validation. For each fold, print the train index and test index. The expected output is:

   Fold:  1
   TRAIN: [2 3 4 5 6 7 8 9] TEST: [0 1]
   
   Fold:  2
   TRAIN: [0 1 4 5 6 7 8 9] TEST: [2 3]
   
   Fold:  3
   TRAIN: [0 1 2 3 6 7 8 9] TEST: [4 5]
   
   Fold:  4
   TRAIN: [0 1 2 3 4 5 8 9] TEST: [6 7]
   
   Fold:  5
   TRAIN: [0 1 2 3 4 5 6 7] TEST: [8 9]