Premium
Use of majority votes in statistical learning
Author(s) -
Zhu Mu
Publication year - 2015
Publication title -
wiley interdisciplinary reviews: computational statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 38
eISSN - 1939-0068
pISSN - 1939-5108
DOI - 10.1002/wics.1362
Subject(s) - popularity , boosting (machine learning) , gradient boosting , cluster analysis , computer science , random forest , aggregate (composite) , exploratory data analysis , machine learning , data science , artificial intelligence , ensemble learning , data mining , psychology , social psychology , materials science , composite material
Today, algorithms such as the gradient boosting machine and the random forest are among the most competitive tools in prediction contests. We review how these algorithms came about. The basic underlying idea is to aggregate predictions from a diverse collection of models. We also explore a few very diverse directions in which the basic idea has evolved, and clarify some common misconceptions that grew as the idea steadily gained its popularity. WIREs Comput Stat 2015, 7:357–371. doi: 10.1002/wics.1362 This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification