Really, Cross-Validation has so many types :)
Cross-validation is a technique that allows us to utilize our data better for training and evaluating the model. For example, while using cross-validation, you effectively use complete data for training the model.
Cross-validation also helps in finding the best hyperparameter for the model.
Cross-Validation in Machine Learning has many types:
2. Leave-p-out cross-validation
4. Stratified k-fold cross-validation
6. Repeated Random Sub-sampling Cross-Validation
7. Exhaustive cross-validation
8. Non-exhaustive Cross-Validation
10. Time Series cross-validation
Top 10 Types of Cross-Validation in Machine Learning is a short video to discuss various types of cross-validation techniques in machine learning and how they are different.
Please watch the below video for detailed information regarding this:
There are many advantages and disadvantages of using cross-validation as well. Please watch the below video to learn about Cross-Validation Advantages and Disadvantages in Machine Learning
Happy Learning :)
