In this tutorial, we're going to talk about the very important concept of Cross Validation and how does it help in avoiding overfitting, making our models more robust, finding the optimal hyperparameters for our data and better generalize to the future data.
We'll be looking at a very popular implementation of cross validation, which is K-Fold Cross validation, step by step. We'll understand in-depth how it works and how do others like Stratified K-Fold, LOO-CV, Repeated K-Fold, Repeated Stratified K-Fold work.
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:
github.com/rachittoshniwal/ma...
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