A surrogate split uses another predictor to approximate the left-versus-right assignments made by a tree node's primary split. It can route observations whose primary predictor is missing. Surrogates are ranked by agreement with the primary split on observations where both predictors are known, accounting for trivial majority assignments. This differs from simply filling a missing predictor with its sample mean.
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