Concept drift

Last revised by Candace Makeda Moore on 10 Mar 2020

Citation, DOI, disclosures and article data

Citation:

Moore C, Concept drift. Reference article, Radiopaedia.org (Accessed on 27 Apr 2024) https://doi.org/10.53347/rID-74939

DOI:

https://doi.org/10.53347/rID-74939

Permalink:

https://radiopaedia.org/articles/74939

rID:

74939

Article created:

10 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:

10 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:

1 time, by 1 contributor - see full revision history and disclosures

Concept drift refers to a phenomenon in the practical application of AI in which some underlying statistics or characteristics of one or more variables changes after the deployment of a model such that a specific AI model's predictive accuracy changes. Concept drift is a problem that can be managed by periodically testing and updating the models or in some cases deploying models that take the possibility of concept drift into account.