Concept
Gradient-boosted decision trees
Gradient-boosted decision trees (GBDT) are an ensemble method that builds many shallow decision trees in sequence, where each new tree fits the residual errors of the current ensemble. Like a random forest, except the trees are not independent: instead of averaging many…
The rest of “Gradient-boosted decision trees” is a premium feature: every concept in the library gets a precise, practitioner-focused write-up like this one, cross-linked straight from the paper summaries that use it.
Log in to unlock→