| CPC G06F 18/241 (2023.01) [G06F 9/5027 (2013.01)] | 20 Claims |
|
1. A machine learning-based method, comprising:
generating, for a set of training entities using a classification machine learning model, outputs that indicate likelihoods of a particular event occurring with respect to the set of training entities, wherein each respective training entity of the set of training entities is associated with a respective label indicating whether the particular event occurred with respect to the respective training entity;
generating, using a given machine learning model, one or more distribution thresholds based on:
approximating a first distribution for the outputs generated by the classification machine learning model;
approximating a second distribution for occurrences of the particular event with respect to the set of training entities; and
generating a given distribution threshold based on minimizing a value for the given distribution threshold, wherein the value for the given distribution threshold is generated as a function of the first distribution, the second distribution, and a targeted rate of occurrence for the particular event with respect to entities having corresponding likelihoods of the particular event occurring that are below the given distribution threshold;
generating, for a received entity using the classification machine learning model, a given output that indicates a likelihood of the particular event occurring with respect to the received entity; and
performing a given intervention with respect to the received entity based on the likelihood for the received entity exceeding the given distribution threshold.
|