MACHINE LEARNING MODEL TRAINING OUTLIERS

Embodiments are disclosed for a method for machine learning model training outliers. The method includes determining multiple metric values for corresponding transactions generated by a machine learning model. The method also includes deleting multiple preliminary outliers from the transactions base...

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Bibliographic Details
Main Authors Erazmus, Maksymilian, Bigaj, Rafal, Sobala, Wojciech, Cmielowski, Lukasz G
Format Patent
LanguageEnglish
Published 23.09.2021
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES
PHYSICS
SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR
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Summary:Embodiments are disclosed for a method for machine learning model training outliers. The method includes determining multiple metric values for corresponding transactions generated by a machine learning model. The method also includes deleting multiple preliminary outliers from the transactions based on a derived cut-off value. Further, the method includes identifying an absolute goal for improving a metric of the machine learning model. Additionally, the method includes identifying multiple training outliers from the remaining transactions. The remaining transactions include the transactions remaining after deleting the preliminary outliers. Also, a metric value of the remaining transactions meets the absolute goal.
Bibliography:Application Number: US202016822081