What is the link between Random Forest and measure concentration phenomena?

Random Forest benefits from concentration of measure as ensemble averages stabilize around expected predictions.

Measure concentration implies that in high-dimensional spaces, random variables (like tree predictions) concentrate near their expected value. As Random Forest aggregates many trees, predictions concentrate around E[T(x)], reducing variance. This phenomenon strengthens generalization in high-dimensional regimes.