StratifiedKFold vs KFold in scikit-learn

Question

I use this code to test KFold and StratifiedKFold.

import numpy as np
from sklearn.model_selection import KFold,StratifiedKFold

X = np.array([
    [1,2,3,4],
    [11,12,13,14],
    [21,22,23,24],
    [31,32,33,34],
    [41,42,43,44],
    [51,52,53,54],
    [61,62,63,64],
    [71,72,73,74]
])

y = np.array([0,0,0,0,1,1,1,1])

sfolder = StratifiedKFold(n_splits=4,random_state=0,shuffle=False)
floder = KFold(n_splits=4,random_state=0,shuffle=False)

for train, test in sfolder.split(X,y):
    print('Train: %s | test: %s' % (train, test))
print("StratifiedKFold done")

for train, test in floder.split(X,y):
    print('Train: %s | test: %s' % (train, test))
print("KFold done")

I found that StratifiedKFold can keep the proportion of labels, but KFold can't.

Train: [1 2 3 5 6 7] | test: [0 4]
Train: [0 2 3 4 6 7] | test: [1 5]
Train: [0 1 3 4 5 7] | test: [2 6]
Train: [0 1 2 4 5 6] | test: [3 7]
StratifiedKFold done
Train: [2 3 4 5 6 7] | test: [0 1]
Train: [0 1 4 5 6 7] | test: [2 3]
Train: [0 1 2 3 6 7] | test: [4 5]
Train: [0 1 2 3 4 5] | test: [6 7]
KFold done

It seems that StratifiedKFold is better, so should KFold not be used?

When to use KFold instead of StratifiedKFold?

Answer 1

Assume Classification problem, Having 3 class(A,B,C) to predict.

Class  No_of_instance

 A           50 
 B           50
 C           50

**StratifiedKFold**

If data-set is  divided  into 5 fold. Then each fold will contains 10 instance from each class, i.e. no of instance per class is equal and follow  uniform distribution.

**KFold**

it will randomly took 30 instance and no of instance per class may or may not be equal or uniform.

**When to use**

Classification task use StratifiedKFold, and regression task use Kfold .
 
But if dataset contains  large number of instance, both StratifiedKFold and Kfold can be used in classification task.

Answer 2

StratifiedKFold: This cross-validation object is a variation of KFold that returns stratified folds. The folds are made by preserving the percentage of samples for each class

KFold: Split dataset into k consecutive folds.

StratifiedKFold is used when is need to balance of percentage each class in train & test. If not required KFOld is used.