Academic Journals Database
Disseminating quality controlled scientific knowledge

Navigating Random Forests and related advances in algorithmic modeling

ADD TO MY LIST
 
Author(s): David S. Siroky

Journal: Statistics Surveys
ISSN 1935-7516

Volume: 3;
Start page: 147;
Date: 2009;
Original page

Keywords: CART | Bagging | Boosting | Random Forests | Algorithmic methods | Non-parametrics | Ensemble and committee methods

ABSTRACT
This article addresses current methodological research on non-parametric Random Forests. It provides a brief intellectual history of Random Forests that covers CART, boosting and bagging methods. It then introduces the primary methods by which researchers can visualize results, the relationships between covariates and responses, and the out-of-bag test set error. In addition, the article considers current research on universal consistency and importance tests in Random Forests. Finally, several uses for Random Forests are discussed, and available software is identified.
RPA Switzerland

Robotic Process Automation Switzerland

    

Tango Jona
Tangokurs Rapperswil-Jona