How does Random Forest behave under implicit feature selection bias amplification?

RF can amplify feature selection bias when certain features consistently dominate impurity reduction.

Even with feature subsampling, strong predictors are more likely to appear in multiple trees and dominate splits. This leads to bias amplification where certain features are repeatedly reinforced, reducing model diversity and potentially overshadowing weaker but informative signals.