What does KFold in python exactly do?

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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:
kaggle cross-validation scikit-learn python
3 Answers

Sharing theoretical information about KF that I have learnt so far.

KFOLD is a model validation technique, where it's not using your pre-trained model. Rather it just use the hyper-parameter and trained a new model with k-1 data set and test the same model on the kth set.

K different models are just used for validation.

It will return the K different scores(accuracy percentage), which are based on kth test data set. And we generally take the average to analyse the model.

We repeat this process with all the different models that we want to analyse. Brief Algo:

  1. Split data in to training and test part.
  2. Trained different models say SVM, RF, LR on this training data.
   2.a Take whole data set and divide in to K-Folds.
   2.b Create a new model with the hyper parameter received after training on step 1.
   2.c Fit the newly created model on K-1 data set.
   2.d Test on Kth data set
   2.e Take average score.
  1. Analyse the different average score and select the best model out of SVM, RF and LR.

Simple reason for doing this, we generally have data deficiencies and if we divide the whole data set into:

  1. Training
  2. Validation
  3. Testing

We may left out relatively small chunk of data and which may overfit our model. Also possible that some of the data remain untouched for our training and we are not analysing the behavior against such data.

KF overcome with both the issues.

user_11473980

The procedure has a single parameter called k that refers to the number of groups that a given data sample is to be split into. As such, the procedure is often called k-fold cross-validation. When a specific value for k is chosen, it may be used in place of k in the reference to the model, such as k=10 becoming 10-fold cross-validation..

You can refer to this post for more information. https://medium.com/@xzz201920/stratifiedkfold-v-s-kfold-v-s-stratifiedshufflesplit-ffcae5bfdf

user_134218620
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