How does Random Forest behave under heterogenous feature noise distributions?

RF is robust to heterogeneous noise but may overfit features with low noise variance.

When different features have different noise levels, RF tends to prefer low-noise features during split selection because they produce higher impurity gains. This introduces implicit feature bias. However, ensemble averaging mitigates overfitting caused by noisy features compared to single trees.