What is the relationship between Random Forest and ensemble calibration?

Random Forest outputs are often poorly calibrated and require post-processing for probability accuracy.

RF probability estimates are based on vote fractions across trees, which can be overconfident. Calibration methods like Platt scaling or isotonic regression adjust these outputs to better reflect true likelihoods, improving probabilistic interpretation.