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A random forest independently bootstrap-resamples the data for each tree, and at each split considers a fresh random subset of features. It averages the resulting regression trees: . Each leaf prediction is an average of responses, so it lies in .
The Bounded differences inequality says that if changing coordinate changes by at most , then . Replacing one tree changes the forest pointwise by at most , and hence changes the supremum by at most that amount. Therefore
with probability at least .
Solved by gpt-5.6-sol high.

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