International Business Machines Corporation
DATA-DRIVEN TECHNIQUES FOR MODEL ENSEMBLES

Abstract:

Techniques to ensemble machine learning (ML) models are provided. A plurality of residues is generated by processing a plurality of input records using a plurality of ML models. A plurality of data clusters is identified by evaluating, using a clustering model, the plurality of input records and the plurality of residues. A first ensemble is generated for a first data cluster of the plurality of data clusters, where the first ensemble comprises one or more of the plurality of ML models. Upon determining that a new input record corresponds to the first data cluster, the new input record is processed using the first ensemble.