Premium
Multivariate random forests
Author(s) -
Segal Mark,
Xiao Yuanyuan
Publication year - 2011
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.12
Subject(s) - random forest , computer science , multivariate statistics , schema (genetic algorithms) , machine learning , artificial intelligence , simple (philosophy) , tree (set theory) , decision tree , data mining , mathematics , epistemology , mathematical analysis , philosophy
Abstract Random forests have emerged as a versatile and highly accurate classification and regression methodology, requiring little tuning and providing interpretable outputs. Here, we briefly outline the genesis of, and motivation for, the random forest paradigm as an outgrowth from earlier tree‐structured techniques. We elaborate on aspects of prediction error and attendant tuning parameter issues. However, our emphasis is on extending the random forest schema to the multiple response setting. We provide a simple illustrative example from ecology that showcases the improved fit and enhanced interpretation afforded by the random forest framework. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 80‐87 DOI: 10.1002/widm.12 This article is categorized under: Algorithmic Development > Hierarchies and Trees Algorithmic Development > Ensemble Methods Technologies > Machine Learning Technologies > Prediction