| CPC G06F 18/24323 (2023.01) [G06N 5/01 (2023.01)] | 6 Claims |
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1. A method for generating a decision tree having a plurality of nodes, arranged hierarchically as parent nodes and child nodes, comprising:
during a training phase for building the decision tree, generating nodes of the decision tree including:
(a) receiving i) training data including data instances, each data instance having a plurality of attributes and a corresponding label, ii) instance weightings, iii) a valid domain for each attribute generated, and iv) an accumulated weighted sum of predictions for a branch of the decision tree, thereby training different branches of the decision tree with disjoint subsets of the training data;
(b) foregoing generating a node having a sibling node with an identical prediction;
(c) associating one of a plurality of binary prediction of an attribute with each node including selecting the one of the plurality of binary predictions having a least amount of weighted error for the valid domain, the weighted error being based on the instance weightings and the accumulated weighted sum of predictions for the branch of the decision tree associated with the node;
(d) in accordance with a determination that the node includes child nodes, repeat the generating the node step for the child nodes; and
(e) in accordance with a determination that the node is a terminal node, associating the terminal node with an outcome classifier,
wherein steps (a), (b), (c), (d) and (e) train the branch of the decision tree; and
displaying the decision tree including the plurality of nodes arranged hierarchically.
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