Gaussian Process Regression (GPR) Representation in Predictive Model Markup Language (PMML)

• Published in: Smart and sustainable manufacturing systems 2017-01, Vol.1 (1), p.121-141
• Main Authors: Park, J, Lechevalier, D, Ak, R, Ferguson, M, Law, K H, Lee, Y-T T, Rachuri, S
• Format: Article
• Language: English
• ISSN: 2572-3928; 2572-3928
• DOI: 10.1520/SSMS20160008

This paper describes Gaussian process regression (GPR) models presented in predictive model markup language (PMML). PMML is an extensible-markup-language (XML) -based standard language used to represent data-mining and predictive analytic models, as well as pre- and post-processed data. The previous PMML version, PMML 4.2, did not provide capabilities for representing probabilistic (stochastic) machine-learning algorithms that are widely used for constructing predictive models taking the associated uncertainties into consideration. The newly released PMML version 4.3, which includes the GPR model, provides new features: confidence bounds and distribution for the predictive estimations. Both features are needed to establish the foundation for uncertainty quantification analysis. Among various probabilistic machine-learning algorithms, GPR has been widely used for approximating a target function because of its capability of representing complex input and output relationships without predefining a set of basis functions, and predicting a target output with uncertainty quantification. GPR is being employed to various manufacturing data-analytics applications, which necessitates representing this model in a standardized form for easy and rapid employment. In this paper, we present a GPR model and its representation in PMML. Furthermore, we demonstrate a prototype using a real data set in the manufacturing domain.