MITIGATING PARTIALITY IN REGRESSION MODELS

A computer device receives historical prediction data, where the historical prediction data includes historical data and corresponding predictions generated for the historical data by a regression machine learning model. The computing device identifies undesired predictions in the historical predict...

Full description

Saved in:
Bibliographic Details
Main Authors Bhide, Manish Anand, Goyal, Prateek, Chamarthy, Ravi Chandra
Format Patent
LanguageEnglish
Published 12.05.2022
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
HANDLING RECORD CARRIERS
PHYSICS
PRESENTATION OF DATA
RECOGNITION OF DATA
RECORD CARRIERS
Online AccessGet full text

Cover

More Information
Summary:A computer device receives historical prediction data, where the historical prediction data includes historical data and corresponding predictions generated for the historical data by a regression machine learning model. The computing device identifies undesired predictions in the historical prediction data based, at least in part, on a perturbation analysis, where the perturbation analysis includes modifying an attribute of the historical data and using the regression machine learning model to generate predictions for the historical data with the modified attribute. The computing device trains a binary classification model to classify predictions as undesired, using the historical prediction data and the identified undesired predictions as training data. The computing device generates a prediction for a new data entry utilizing the regression machine learning model and the binary classification model.
Bibliography:Application Number: US202017093854