Uncertainty quantification for machine learning classification modelling

Abstract

Certain aspects of the disclosure provide a method, comprising: processing input data with an ensemble of nonlinear machine learning models; generating a sparse high-dimensional embedding based on one or more leaf nodes of each nonlinear machine learning model in the ensemble of nonlinear machine learning models; projecting the high-dimensional embedding into a lower-dimensional embedding, wherein the lower-dimensional embedding is less sparse than the high-dimensional embedding; processing the lower-dimensional embedding with a linear machine learning model to generate a binary class prediction; determining a confidence for the binary class prediction; and outputting: the binary class prediction if the confidence is greater than or equal to a threshold; or a flipped binary class prediction if the confidence is lower than the threshold.