How does Random Forest behave under label distribution shift?

Performance degrades when class priors change significantly between training and testing.

Random Forest learns decision boundaries based on observed class proportions. If label distribution shifts, probability calibration becomes invalid, even if ranking remains partially correct. This affects threshold-based decision systems more severely than ranking-based metrics.