Content deleted Content added
m remove unnecessary sentence-breaking phrase |
No edit summary |
||
Line 1:
{{Short description|Statistical regression model}}
{{About|the statistical method|additive color models|Additive color}}
In [[statistics]], an '''additive model''' ('''AM''') is a [[nonparametric regression]] method. It was suggested by [[Jerome H. Friedman]] and Werner Stuetzle (1981)<ref>[[Friedman, J.H.]] and Stuetzle, W. (1981). "Projection Pursuit Regression", ''Journal of the American Statistical Association'' 76:817–823. {{doi|10.1080/01621459.1981.10477729}}</ref> and is an essential part of the [[Alternating conditional expectations|ACE]] algorithm. The ''AM'' uses a one-dimensional [[Smoothing|smoother]] to build a restricted class of nonparametric regression models. Because of this, it is less affected by the [[curse of dimensionality]] than a ''p''-dimensional smoother. Furthermore, the ''AM'' is more flexible than a [[linear regression|standard linear model]], while being more interpretable than a general regression surface at the cost of approximation errors. Problems with ''AM'', like many other machine-learning methods, include [[model selection]], [[overfitting]], and [[multicollinearity]].
| |||