The random_state argument in BorutaShap.fit is set to 0 by default. This means that the train/test split performed in Check_if_chose_train_or_test_and_train_model is the same for every iteration. For the shadow features this doesn't really matter, but for the real features is means that the same subset of the data is used for training at every iteration. Is this by design, or would it be better to perform a random split each iteration?
Randomize train/test split
The
random_stateargument inBorutaShap.fitis set to 0 by default. This means that the train/test split performed inCheck_if_chose_train_or_test_and_train_modelis the same for every iteration. For the shadow features this doesn't really matter, but for the real features is means that the same subset of the data is used for training at every iteration. Is this by design, or would it be better to perform a random split each iteration?