How does Random Forest behave in presence of heteroscedastic label noise?

RF performance varies across regions where label noise depends on input space.

In heteroscedastic label noise, the probability of incorrect labels varies with X. RF may overfit low-noise regions while underperforming in high-noise regions due to impurity-driven splits favoring cleaner partitions. This creates uneven decision boundary reliability across feature space.