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Table 1 Differences between the implementations of cut-point identification at a current node, for various instantiations of growTree

From: A method combining a random forest-based technique with the modeling of linkage disequilibrium through latent variables, to run multilocus genome-wide association studies

 
  1. (A) growDecisionTree. (B) growRFTree. (C) growExtraTree. Functions growDecisionTree, growRFTree and growExtraTree are the instantiations of the generic function growTree (Algorithm 1), in the standard decision tree learning context, the random forest learning context, and the Extremely randomized tree (Extra-tree) context, respectively. Functions growDecisionTree and growRFTree are respectively detailed in Algorithm 2 (main text) and Algorithm 7 (Appendix). Complexity decreases across the three compared functions from exact optimization on the whole set V of variables, through exact optimization restrained to a random subset V aleat of V, and to optimization over the cut-points selected at random for the variables in a random subset V aleat