Cross validation

Last revised by Candace Makeda Moore on 12 Mar 2020

Citation, DOI, disclosures and article data

Citation:

Moore C, Cross validation. Reference article, Radiopaedia.org (Accessed on 23 Apr 2024) https://doi.org/10.53347/rID-74964

DOI:

https://doi.org/10.53347/rID-74964

Permalink:

https://radiopaedia.org/articles/74964

rID:

74964

Article created:

11 Mar 2020, Candace Makeda Moore

Disclosures:

At the time the article was created Candace Makeda Moore had no recorded disclosures.

View Candace Makeda Moore's current disclosures

Last revised:

12 Mar 2020, Candace Makeda Moore

Disclosures:

At the time the article was last revised Candace Makeda Moore had no recorded disclosures.

View Candace Makeda Moore's current disclosures

Revisions:

2 times, by 1 contributor - see full revision history and disclosures

Cross validation refers to a set of statistical methods for the assessment of machine learning models or variables and features inside them. There are several variations on these methods, e.g. k-fold or leave-p-out cross validation. Generally if a method is called by a number e.g. four-fold cross validation, then it is an example of a k-fold method. Practically speaking, usually cross validation is used to overcome the potential bias in test and training data sets to get a well fit model; however it can be used in feature selection as well.