This article describes a R package Boruta, implementing a novel feature selection algorithm for nding all relevant variables. The algorithm is designed as a wrapper around a Random Forest classication 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. 本文介紹了一個R包Boruta，實現了一種尋找所有相關變量的新特征選擇算法。 該算法被設計為包裝器隨機森林分類算法。 它迭代地刪除了那些通過統計檢驗證明與隨機探針不太相關特征。 Boruta包為算法提供了方便的接口。本文是對 算法的簡短描述并介紹了其應用實例。