Hindi-Types Of Cross Validation In Machine Learning|Krish Naik

Cross-Validation also referred to as out of sampling technique is an essential element of a data science project. It is a resampling procedure used to evaluate machine learning models and access how the model will perform for an independent test dataset. Below are the types of cross validation.
Leave one out cross-validation
Holdout cross-validation
Repeated random subsampling validation
k-fold cross-validation
Stratified k-fold cross-validation
Time Series cross-validation
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