> For the complete documentation index, see [llms.txt](https://digitalgarden.batamladen.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://digitalgarden.batamladen.com/notes/machine-learning/training-and-testing-data/cross-validation.md).

# Cross Validation

## What is cross validation?

Cross-validation is a more robust method to evaluate the model's performance. It involves splitting the data into `k` subsets (folds) and performing the training and testing `k` times, each time using a different fold as the test set and the remaining folds as the training set. This provides multiple performance estimates, reducing the risk of overfitting.

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## Types of Cross-Validation

* **k-Fold Cross-Validation**
* **Stratified k-Fold Cross-Validation**
* Leave-One-Out Cross-Validation (LOOCV)
* Time Series Cross-Validation

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