---
title: What is k-fold cross-validation?
description: What is k-fold cross-validation?
---
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# What is k-fold cross-validation?
Cross-validation is a resampling procedure used to evaluate machine learning models on a limited data sample.
The procedure has a single parameter called k that refers to the number of groups that a given data sample is to be split into. As such, the procedure is often called k-fold cross-validation. When a specific value for k is chosen, it may be used in place of k in the reference to the model, such as k=10 becoming 10-fold cross-validation.
Cross-validation is primarily used in applied machine learning to estimate the skill of a machine learning model on unseen data. That is, to use a limited sample in order to estimate how the model is expected to perform in general when used to make predictions on data not used during the training of the model.
It is a popular method because it is simple to understand and because it generally results in a less biased or less optimistic estimate of the model skill than other methods, such as a simple train/test split.
The general procedure is as follows:
Shuffle the dataset randomly.
Split the dataset into k groups
For each unique group:
Take the group as a hold out or test data set
Take the remaining groups as a training data set
Fit a model on the training set and evaluate it on the test set
Retain the evaluation score and discard the model
Summarize the skill of the model using the sample of model evaluation scores
Importantly, each observation in the data sample is assigned to an individual group and stays in that group for the duration of the procedure. This means that each sample is given the opportunity to be used in the hold out set 1 time and used to train the model k-1 times.
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