Random Forests: some methodological insights

TL;DR

This paper provides experimental insights into random forests, focusing on variable importance and selection strategies, especially in high-dimensional settings, to improve interpretation and prediction.

Contribution

It offers new insights into variable importance behavior and proposes a stepwise variable selection method using random forest importance scores.

Findings

Variable importance index behavior clarified

Effective variable selection strategies proposed

Insights applicable to high-dimensional data

Abstract

This paper examines from an experimental perspective random forests, the increasingly used statistical method for classification and regression problems introduced by Leo Breiman in 2001. It first aims at confirming, known but sparse, advice for using random forests and at proposing some complementary remarks for both standard problems as well as high dimensional ones for which the number of variables hugely exceeds the sample size. But the main contribution of this paper is twofold: to provide some insights about the behavior of the variable importance index based on random forests and in addition, to propose to investigate two classical issues of variable selection. The first one is to find important variables for interpretation and the second one is more restrictive and try to design a good prediction model. The strategy involves a ranking of explanatory variables using the random forests score of importance and a stepwise ascending variable introduction strategy.