ASSESSMENT OF MACHINE LEARNING PERFORMANCE WITH LIMITED TEST DATA

Embodiments of the present invention disclose a method, computer program product, and system for mitigating machine learning performance digression due to insufficient test data availability. A set of data is received, wherein the received set of data is parsed into a set of training data and a set...

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Bibliographic Details
Main Authors Cakmak, Umit M, Cmielowski, Lukasz G
Format Patent
LanguageEnglish
Published 30.05.2019
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
PHYSICS
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Summary:Embodiments of the present invention disclose a method, computer program product, and system for mitigating machine learning performance digression due to insufficient test data availability. A set of data is received, wherein the received set of data is parsed into a set of training data and a set of test data. A trained model is generated and the trained model is applying to the set of test data. A first set of performance values of the tested trained model are recorded and, if above a threshold, associated with a performance baseline value. A set of modified test data is generated and the trained model is applied to the set of modified test data. A second set of performance values are recorded and a performance difference value is calculated based on the performance baseline value and second set of recorded performance values. A table of results is generated, for display.
Bibliography:Application Number: US201715825325