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We estimated the optimal number of CARTs to include in the ensemble (M) as well as the optimal tree depth (d) using the bootstrap.
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Tree 1: optimal tree from RAxML search, tree 2: optimal tree from MP ratchet search, where Byblidaceae appear as sister to Lentibulariaceae.
Optimal classification was achieved using RFs of 500 decision trees with unlimited tree depth and four random features.
After applying our algorithm to block-based connected components labeling, an optimal tree is produced containing only 86 leaf nodes with 12 levels for the depth of a tree.
With a random tree model, we show that, as the tree depth approaches infinity, the expected number of nodes expanded by branch-and-bound (BnB) using ε-transformation is at most cubic in the search depth, and that the relative error of the solution cost found with respect to the optimal solution cost is bounded above by a small constant.
b Max tree depth with accuracy.
During each iteration of the algorithm, the decoder may move forward (increase depth within the tree), move backward (reduce depth), or stay at the current tree depth.
We compare redundancy and average backup path length of shared protection trees with optimal tree designs and non-tree designs.
In addition, the decision tree depth is limited in order to simplify the analysis of the tree rules.
This paper systematically investigates how tree structure parameters (the number of leaves, branching factor, tree depth) and visualisation properties influence the tree comprehensibility.
The two trees also differ in their tree depth.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com