difference between StratifiedKFold and StratifiedShuffleSplit in sklearn

In KFolds, each test set should not overlap, even with shuffle. With KFolds and shuffle, the data is shuffled once at the start, and then divided into the number of desired splits. The test data is always one of the splits, the train data is the rest.

In ShuffleSplit, the data is shuffled every time, and then split. This means the test sets may overlap between the splits.

See this block for an example of the difference. Note the overlap of the elements in the test sets for ShuffleSplit.

splits = 5

tx = range(10)
ty = [0] * 5 + [1] * 5

from sklearn.model_selection import StratifiedShuffleSplit, StratifiedKFold
from sklearn import datasets

kfold = StratifiedKFold(n_splits=splits, shuffle=True, random_state=42)
shufflesplit = StratifiedShuffleSplit(n_splits=splits, random_state=42, test_size=2)

print("KFold")
for train_index, test_index in kfold.split(tx, ty):
    print("TRAIN:", train_index, "TEST:", test_index)

print("Shuffle Split")
for train_index, test_index in shufflesplit.split(tx, ty):
    print("TRAIN:", train_index, "TEST:", test_index

output:

KFold
TRAIN: [0 2 3 4 5 6 7 9] TEST: [1 8]
TRAIN: [0 1 2 3 5 7 8 9] TEST: [4 6]
TRAIN: [0 1 3 4 5 6 8 9] TEST: [2 7]
TRAIN: [1 2 3 4 6 7 8 9] TEST: [0 5]
TRAIN: [0 1 2 4 5 6 7 8] TEST: [3 9]
Shuffle Split
TRAIN: [8 4 1 0 6 5 7 2] TEST: [3 9]
TRAIN: [7 0 3 9 4 5 1 6] TEST: [8 2]
TRAIN: [1 2 5 6 4 8 9 0] TEST: [3 7]
TRAIN: [4 6 7 8 3 5 1 2] TEST: [9 0]
TRAIN: [7 2 6 5 4 3 0 9] TEST: [1 8]

As for when to use them, I tend to use KFolds for any cross validation, and I use ShuffleSplit with a split of 2 for my train/test set splits. But I'm sure there are other use cases for both.