Correlation and variable importance in random forests

Understand

This paper is about variable selection with the random forests algorithm in presence of correlated predictors.

• In high-dimensional regression or classification frameworks, variable selection is a difficult task, that becomes even more challenging in the presence of highly correlated predictors.
• Firstly we provide a theoretical study of the permutation importance measure for an additive regression model.
• This allows us to describe how the correlation between predictors impacts the permutation importance.