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Lecture
Decision and regression trees
Description
This lecture covers decision and regression trees, including the principles of tree predictors, impurity measures, empirical entropies, impurity decrease via a split, learning algorithms, and implementations. It also discusses conditional inference trees and tree pruning techniques.
Official source
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