System and method for classifying datasets to guide action

A system for classifying datasets, the system comprising:

• a computing device;
• a memory;
• and
• a processor, the processor configured to:
• submit alert data items of one or more datasets to a ML model that is a decision tree, wherein the alert data items of each dataset comprise:
• an alert rule that initiated an alert for the dataset comprising a first set of thresholds, one or more data values assessed by the first set of thresholds in the generation of the alert, and an alert categorization of the alert selected from a group consisting of:
• a true positive categorization and a false positive categorization;
• assess, by the ML model, combinations of the alert categorization in relation to the one or more data values and the alert rule;
• generate, by the ML model, a second set of thresholds for the one or more data values, wherein the second set of thresholds has a reduced false positive categorization of the alerts compared to the first set of thresholds and wherein the second set of thresholds is based on reinforcement learning applied with the ML model, wherein the reinforcement learning is based on ranking the second set of thresholds based on generated threshold values;
• and
• update the alert rule to comprise the second set of thresholds.

A method of dynamically categorizing customer datasets, the method comprising:

• submitting, by a computer, categorization data items of a plurality customer datasets to a ML model that is a decision tree, wherein the categorization data items of each dataset of the plurality of customer datasets comprise:
• a categorization rule that initiated a categorization for the customer dataset comprising a first set of ranges, wherein the categorization is selected from a group consisting of:
• a true positive categorization and false positive categorization, and one or more data values assessed by the first set of ranges in the generation of the categorization;
• determining, by the ML model, by the computer, combinations of the categorization in relation to the one or more data values and the categorization rule;
• generating, the ML model, by the computer, a second set of ranges for the one or more data values, wherein the second set of ranges has a reduced false positive categorization of the categorizations compared to the first set of ranges and wherein the second set of thresholds is based on reinforcement learning applied with the ML model, wherein the reinforcement learning is based on ranking the second set of thresholds based on generated threshold values;
• and
• replacing, by the computer, the first set of ranges with the second set of ranges.