ENSEMBLE MACHINE LEARNING MODELS INCORPORATING A MODEL TRUST FACTOR

• Main Authors: BARTLOW, Nicholas D, KALKA, Nathan, ANDERSON, Janet
• Format: Patent
• Language: English
• Published: 09.02.2023
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; PHYSICS

Methods for improving the prediction accuracy for an ensemble machine learning model are described. In some instances, the methods comprise: (i) receiving data characterizing levels of trust in one or more machine learning models that form the ensemble machine learning model; (ii) calculating a prediction error estimate for each of the one or more machine learning models based on a trust score for that machine learning model and relative weights calculated for the data points in a training data set used to train that machine learning model; (iii) calculating a normalized weight for each of the one or more machine learning models using the prediction error estimate calculated for each; and (iv) adjusting an output prediction equation for the ensemble machine learning model, where the adjustment is based, at least in part, on the normalized weights calculated in for each of the one or more machine learning models.