Random forest rule generator

Abstract

In an example embodiment, a random forest machine learning algorithm is used to create and/or identify rules to apply to an individual entity in a computer system that has a plurality of entities, each with a number of rules. More precisely, rule predicates are used as features of a random forest model built to predict a particular outcome (e.g., a transaction that is fraudulent). Hyperparameters of the random forest model are varied and iterated. A classifier is used to calculate feature importance for all features in the training data. Feature importance may be calculated using permutation feature importance. The N 'most important' features are then found from this set. The N “most important” features are then used to find rules above a certain precision and recall rate. These rules may then be backtested and the best rules can be used to generate additional rules.