Top 8 Cross Validation methods!!!

A cross validation is a powerful tool in machine learning used to evaluate a model’s performance. It is a technique used to ensure that a model is generalizable and not overfitting to the training data. Cross-validation is a resampling technique that splits the data into multiple subsets and then uses each subset to train and test the model. This allows the model to be tested on data that it has not seen before, helping to identify any overfitting issues.


Chapters
02:22 K-Fold Cross Validation
06:05 Repeated K-Fold cross Validation
08:18 Leave One Out Cross Validation
10:21 leave P Out Cross Validation
12:06 Stratified K-Fold Cross Validation
14:32 Group K-Fold Cross Validation
16:34 Stratified Group K-Fold Cross Validation
18:23 Time Series Split




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