特征选择
计算机科学
R包
算法
随机森林
选择(遗传算法)
接口(物质)
特征(语言学)
人工智能
数据挖掘
模式识别(心理学)
程序设计语言
并行计算
语言学
最大气泡压力法
哲学
气泡
作者
Miron B. Kursa,Witold R. Rudnicki
标识
DOI:10.18637/jss.v036.i11
摘要
This article describes a R package Boruta, implementing a novel feature selection algorithm for finding emph{all relevant variables}. The algorithm is designed as a wrapper around a Random Forest classification algorithm. It iteratively removes the features which are proved by a statistical test to be less relevant than random probes. The Boruta package provides a convenient interface to the algorithm. The short description of the algorithm and examples of its application are presented.
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