Alternating conditional expectations: Difference between revisions

Revision as of 22:25, 27 January 2025

Revision as of 01:07, 12 February 2025

Line 2:

In [[statistics]], '''Alternating Conditional Expectations (ACE)''' is ana [[nonparametric statistics|nonparametric]] [[algorithm]] used in [[regression analysis]] to find the optimal transformations for both the outcome ([[response variable|response]]) variable and the input (predictor) variables.<ref>Breiman, L. and Friedman, J. H. [http://apps.dtic.mil/dtic/tr/fulltext/u2/a123908.pdf Estimating optimal transformations for multiple regression and correlation]. J. Am. Stat. Assoc., 80(391):580–598, September 1985b. {{PD-notice}}</ref>

For example, in a model that tries to predict house prices based on size and ___location, ACE helps by figuring out if, for instance, transforming the size (maybe taking the square root or logarithm) or the ___location (perhaps grouping locations into categories) would make the relationship easier to model and lead to better predictions. The algorithm iteratively adjusts these transformations until it finds the ones that maximize the predictive power of the regression model.