Newer classification and regression tree techniques: Bagging and Random Forests for ecological prediction

Anantha M. Prasad; Louis Iverson; Andy Liaw; Andy Liaw

Treesearch

Newer classification and regression tree techniques: Bagging and Random Forests for ecological prediction

Informally Refereed

Authors:

Anantha M. Prasad, Louis Iverson, Andy Liaw, Andy Liaw

Year:

2006

Type:

Miscellaneous Publication

Station:

Northern Research Station

Source:

Ecosystems. (9): 181-199.

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Abstract

We evaluated four statistical models - Regression Tree Analysis (RTA), Bagging Trees (BT), Random Forests (RF), and Multivariate Adaptive Regression Splines (MARS) - for predictive vegetation mapping under current and future climate scenarios according to the Canadian Climate Centre global circulation model.

Keywords

predictive mapping, data mining, classification and regression trees (CART), Regression Tree Analysis (RTA), decision tree, Multivariate Adaptive Regression Splines (MARS), Bagging Trees, Random Forests, Kappa, fuzzy Kappa, Canadian Climate Centre (CCC), global circulation model (GCM), eastern United State

Citation

Prasad, Anantha M.; Iverson, Louis R.; Liaw, Andy. 2006. Newer classification and regression tree techniques: Bagging and Random Forests for ecological prediction. Ecosystems. (9): 181-199.