scikit learn - What does KFold in python exactly do?

Question

I am looking at this tutorial: https://www.dataquest.io/mission/74/getting-started-with-kaggle

I got to part 9, making predictions. In there there is some data in a dataframe called titanic, which is then divided up in folds using:

# Generate cross validation folds for the titanic dataset.  It return the row indices corresponding to train and test.
# We set random_state to ensure we get the same splits every time we run this.
kf = KFold(titanic.shape[0], n_folds=3, random_state=1)

I am not sure what is it exactly doing and what kind of object kf is. I tried reading the documentation but it did not help much. Also, there are three folds (n_folds=3), why is it later only accessing train and test (and how do I know they are called train and test) in this line?

for train, test in kf:

Answer 1

KFold will provide train/test indices to split data in train and test sets. It will split dataset into k consecutive folds (without shuffling by default).Each fold is then used a validation set once while the k - 1 remaining folds form the training set (source).

Let's say, you have some data indices from 1 to 10. If you use n_fold=k, in first iteration you will get i'th (i<=k) fold as test indices and remaining (k-1) folds (without that i'th fold) together as train indices.

An example

import numpy as np
from sklearn.cross_validation import KFold

x = [1,2,3,4,5,6,7,8,9,10,11,12]
kf = KFold(12, n_folds=3)

for train_index, test_index in kf:
    print (train_index, test_index)

Output

Fold 1: [ 4 5 6 7 8 9 10 11] [0 1 2 3]

Fold 2: [ 0 1 2 3 8 9 10 11] [4 5 6 7]

Fold 3: [0 1 2 3 4 5 6 7] [ 8 9 10 11]

Import Update for sklearn 0.20:

KFold object was moved to the sklearn.model_selection module in version 0.20. To import KFold in sklearn 0.20+ use from sklearn.model_selection import KFold. KFold current documentation source