Generating rule lists from tree ensemble models

A system comprising:

• one or more hardware processors;
• and
• a memory storing instructions that, when executed by the one or more hardware processors, cause the one or more hardware processors to perform operations comprising:
• extracting, by a rule generator of a server, first stage candidate rules from individual trees of a tree ensemble comprising a plurality of trees associated with a data set;
• identifying, by an evaluation module of the server, first stage candidate rules that satisfy a precision threshold, the first stage candidate rules that satisfy the precision threshold being placed in a solution set and pruned for further consideration;
• determining whether a further stage is needed based on whether a predetermined number of positive data samples of the data set are covered by the solution set;
• based on a determination that further stages are needed, perform operations comprising:
• generating, by the rule generator, next stage candidate rules from previous stage candidate rules that have not been pruned;
• and
• identifying, by the evaluation module, the next stage candidate rules that satisfy the precision threshold, the next stage candidate rules that satisfy the precision threshold being placed in the solution set and pruned for further consideration;
• and
• generating, by a list generator of the server, a simplified rule list by identifying a minimum subset of rules in the solution set that covers the positive data samples of the data set within the precision threshold.

A machine-storage medium storing instructions that, when executed by at least one hardware processor of a machine, cause the machine to perform operations comprising:

• extracting, by a rule generator of a server, first stage candidate rules from individual trees of a tree ensemble comprising a plurality of trees associated with a data set;
• identifying, by an evaluation module of the server, first stage candidate rules that satisfy a precision threshold, the first stage candidate rules that satisfy the precision threshold being placed in a solution set and pruned for further consideration;
• determining whether a further stage is needed based on whether a predetermined number of positive data samples of the data set are covered by the solution set;
• based on a determination that further stages are needed, perform operations comprising:
• generating, by the rule generator, next stage candidate rules from previous stage candidate rules that have not been pruned;
• and
• identifying, by the evaluation module, the next stage candidate rules that satisfy the precision threshold, the next stage candidate rules that satisfy the precision threshold being placed in the solution set and pruned for further consideration;
• and
• generating, by a list generator of the server, a simplified rule list by identifying a minimum subset of rules in the solution set that covers the positive data samples of the data set within the precision threshold.