A Proof of Theorem

Proposition 2. Using the same notations as in Proposition 1, we have the following results. Algorithm 2 gives pseudocode for finding the optimal split for a given feature. Output: Split (f, t) that gives the largest risk reduction. Proposition 5. F or the sigmoid loss, we have null R Proposition 4. If a node contains the examples Output: Collection of trained decision trees. Algorithm 5: Find_Split(κ, F, T) Input: κ - node; F - number of attributes; T - number of threshold values per attribute.