Cross Validation Guide- K-Folds, Stratified K-Folds, Leave-one-out and Leave-p-out Cross Validations

In this comprehensive video guide, we dive into the world of cross-validation techniques used in machine learning and data science. Join us as we explore four popular methods: K-Folds, Stratified K-Folds, Leave-One-Out, and Leave-P-Out cross validations.

We'll start by understanding the fundamental concept of cross-validation and why it's essential for model evaluation and selection. Then, we'll explore the K-Folds technique, which involves dividing the dataset into K subsets and iteratively training and evaluating the model on different combinations of these subsets.

Cross Validation Guide- K-Folds, Stratified K-Folds, Leave-one-out and Leave-p-out Cross Validations

The below topics are explained in this Machine Learning basics video:
1. Introduction ( 00:00 )
2. What is Cross-Validation in Machine Learning?( 00:10 )
2. Types of Cross-Validation ( 01:43 )
3. K-Folds Cross Validation( 02:01 )
4. Stratified K-Folds Cross Validation( 03:50 )
5. Leave-one-out Cross Validation ( 06:05 )
6. Leave-p-out Cross Validation ( 08:22 )
7. Outro (09:32)

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QUERIES SOLVED:
▪ Cross Validation Complete Guide
▪ Types of Cross-Validations
▪ What is Stratified Cross Validation
▪ What is Leave-p-out and Leave-one-out cross Validations?
▪ Complete tutorial about cross-validations in ml


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