(Code) K-Fold, Stratified, Leave One Out, Repeated K-Fold Cross Validation Python | Machine Learning

#cross #validation #techniques

In this tutorial, we're going to implement various types of Cross Validation techniques in Python.

Video contents:
02:07 K-Fold CV
13:23 Stratified K-Fold CV
23:07 LeaveOneOut CV
26:04 Repeated K-Fold CV
29:30 How to see different available metrics?
We'll first look at a very popular implementation of cross validation, which is K-Fold Cross Validation, step by step. We'll understand in-depth how to code it out and how do other variants like Stratified K-Fold, LeaveOneOut-CV, Repeated K-Fold, Repeated Stratified K-Fold work.

We'll also look at the sweet little cross_val_score function and how it helps us greatly by doing most of the heavy lifting of splitting up the dataset into folds, doing the iterations and returning us the scores for each of the iterations.

Cross Validating on our data help us in avoiding overfitting, making our models more robust, finding the optimal hyperparameters for our data and better generalize to the future unseen data.

These topics are so important that they should be grasped before learning how to fit a machine learning algorithm to a dataset and celebrate when the accuracy shows 98%!

I've uploaded all the relevant code and datasets used here (and all other tutorials for that matter) on my github page which is accessible here:

Link:

https://github.com/rachittoshniwal/machineLearning

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Thank you!

Видео (Code) K-Fold, Stratified, Leave One Out, Repeated K-Fold Cross Validation Python | Machine Learning канала Rachit Toshniwal