How does Random Forest behave under feature-dependent label noise models?

RF struggles when label noise depends on specific feature regions.

Feature-dependent label noise introduces systematic corruption where certain regions of feature space have higher mislabeling probability. RF may incorrectly learn these corrupted regions as valid decision boundaries, reinforcing bias. This violates the assumption that noise is independent of features and leads to distorted impurity-based splits.