Optimization of Polynomial Functions on the NuSVR Algorithm Based on Machine Learning: Case Studies on Regression Datasets
(1) Universitas Dian Nuswantoro, Indonesia
(2) Universitas Dian Nuswantoro, Indonesia
(3) Universitas Dian Nuswantoro, Indonesia
(4) Universitas Dian Nuswantoro, Indonesia
(5) Universitas Dian Nuswantoro, Indonesia
Abstract
Purpose: Experimental studies are usually costly, time-consuming, and resource-intensive when it comes to investigating prospective corrosion inhibitor compounds. Machine learning (ML) based on the quantitative structure-property relationship model (QSPR) has become a massive method for testing the effectiveness of chemical compounds as corrosion inhibitors. The main challenge in the ML method is to design a model that produces high prediction accuracy so that the properties of a material can be predicted accurately. In this study, we examine the performance of polynomial functions in the ML-based NuSVR algorithm in evaluating the regression dataset of corrosion inhibition efficiency of pyridine-quinoline compounds.
Methods: Polynomial functions for NuSVR algorithm-based ML.
Result: The outcomes demonstrate that the NuSVR model's prediction ability is greatly enhanced by the application of polynomial functions.
Originality: The combination of polynomial functions and deep machine learning based NuSVR algorithms to increase the accuracy of predictive models.
Keywords
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