	US 12,216,738 B2
	Predicting performance of machine learning models
Lukasz G. Cmielowski, Cracow (PL); Rafal Bigaj, Cracow (PL); Wojciech Sobala, Cracow (PL); and Maksymilian Erazmus, Zasów (PL)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on Oct. 12, 2020, as Appl. No. 17/068,226.
Prior Publication US 2022/0114401 A1, Apr. 14, 2022
Int. Cl. G06F 18/21 (2023.01); G06F 18/214 (2023.01); G06N 20/00 (2019.01); G06Q 10/0639 (2023.01)

CPC G06F 18/2185 (2023.01) [G06F 18/214 (2023.01); G06N 20/00 (2019.01); G06Q 10/06393 (2013.01)]

13 Claims

1. A computer-implemented method for predicting an impact of an adjustment to a machine learning model to key performance indicators, the method comprising:

receiving a proposed adjustment to a machine learning model;

calculating, using a regression machine learning model to ingest the proposed adjustment, a set of value components for a key performance indicator (KPI) as indicator values using input data on a specified schedule;

mapping the calculated indicator values onto scoring payload data;

calculating a plurality of results for the KPI using the set of value components;

automatically determining whether the plurality of results exceeds a performance threshold;

recommending the proposed adjustment based on the determination; and

training the regression model by iteratively performing:

receiving a model scoring payload, an input data set, and a target data set;

calculating a gradient that is a difference between an input data value of the input data set and a target data value of the target data set; and

propagating the gradient through layers of the regression model to update synaptic weights of the regression model.