> For the complete documentation index, see [llms.txt](https://nag-9-s.gitbook.io/machine-learning/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://nag-9-s.gitbook.io/machine-learning/learning/evaluation-metrics/model-validation.md).

# Model Validation

K-fold cross-validationis a method of model validation. It consists of dividing the dataset into k subsets of roughly equal size and training k models, excluding a different subset each time. The excluded subsets are used as the validation set and the union of all the remaining subsets as the training set.

**For each set of parameters you want to validate, train all k models and calculate the mean error across all k models. Finally, you choose the set of parameters giving you the smallest average error.**

Gollapudi - practical machine learning