Forecasting and mitigating concept drift using natural language processing

Abstract

An embodiment for automatically forecasting and mitigating concept drift in machine learning models using natural language processing. The embodiment may automatically detect features and variables considered by a target machine learning model. The embodiment may automatically store and update a configurable corpus of relevant documents pertaining to a domain of the target machine learning model. The embodiment may automatically extract event types and corresponding time stamps from the configurable corpus of relevant documents. The embodiment may automatically correlate the extracted event types with drift detection probabilities to detect expected concept drift in the target machine learning model at a given timestamp. The embodiment may automatically generate an ensemble of replacement models based on probabilities derived from a historical database of models including corresponding keys given by event types. The embodiment may automatically retrain or replace the target machine learning model at the given timestamp.