Supplementary Material for the Paper " Joints in Random Forests "
–Neural Information Processing Systems
Let f be a DT classifier and p(Y | x) be a corresponding GeDT classifier, where each leaf in GeDT is class-factorised, i.e. of the form p(Y)p(X), and where p(Y) has been estimated in the maximum-likelihood sense. Then f(x) = p(Y | x), provided that p(x) > 0. Proof. Recall that the leaves in the GeDT are in one-to-one correspondence with the leaf cells A of the DT, and that the support of any leaf is given by its corresponding A A. Let v Since the GeDT is deterministic, it has at most one non-zero child. Before proving Theorem 2 we need to introduce some background. This theorem extends consistency results for collections of partitions of the state space X, as discussed by Lugosi and Nobel [12].
Neural Information Processing Systems
May-29-2025, 22:37:32 GMT
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