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OPTIMIZED DECISION TREE BASED MODELS

  • US 20150379426A1
  • Filed: 08/19/2014
  • Published: 12/31/2015
  • Est. Priority Date: 06/30/2014
  • Status: Active Grant
First Claim
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1. A system, comprising:

  • one or more computing devices configured to;

    identify one or more run-time optimization goals for a decision-tree based machine learning model to be trained using a data set, including at least a goal for a memory footprint of an execution of the machine learning model subsequent to a training phase of the machine learning model;

    store, in a depth-first order at one or more persistent storage devices during a tree-construction pass of the training phase, respective representations of a plurality of nodes generated for a particular decision tree using at least a portion of the data set;

    determine, for one or more nodes of the particular decision tree during the tree-construction pass, a respective value of a predictive utility metric (PUM), wherein a particular PUM value associated with a particular node of the one or more nodes is a measure of an expected contribution of the particular node to a prediction generated using the machine learning model;

    generate, during a tree-pruning pass of the training phase, a modified version of the particular decision tree, wherein to generate the modified version, at least the particular node is removed from the particular decision tree, wherein the particular node is selected for removal based at least in part on the one or more run-time optimization goals and based at least in part on the particular PUM value;

    store a representation of the modified version of the particular decision tree; and

    subsequent to the training phase, execute the machine learning model using at least the modified version of the particular decision tree to obtain a particular prediction.

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