| CPC G06N 20/00 (2019.01) [G06F 16/2246 (2019.01); G06N 5/02 (2013.01)] | 20 Claims |

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1. A computer-implemented method within a computer hardware system including a machine learning engine and a predictor indicator importance generator, comprising:
generating, using the machine learning engine, a prediction model having
a tree structure including a plurality of nodes,
a plurality of class label subgroups, and
a plurality of class-specific predictor indicators;
causing a first display, in a graphical user interface interacting with the computer hardware system, of a representation of the tree structure including the plurality of class label subgroups and the plurality of class-specific predictor indicators;
receiving, based upon a user input, a selected class label subgroup from the displayed plurality of class label subgroups and a selected class-specific predictor indicator from the displayed plurality of class-specific predictor indicators;
generating, using the predictor indicator importance generator, a class-specific predictor indicator importance for the selected class-specific predictor indicator by merging a term predictor indicator frequency and a purity predictor indicator frequency at each instance of the selected class-specific predictor indicator within one or more nodes of the tree structure; and
causing a second display, in the graphical user interface, of the representation of the tree structure, wherein each path between a root node of the tree structure and a leaf node mapped to the selected class label subgroup having a node containing an instance of the selected class-specific predictor indicator is depicted in a manner that contrasts with other paths between the root node and other leaf nodes of the tree structure.
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