Optimization of Polynomial Functions on the NuSVR Algorithm Based on Machine Learning: Case Studies on Regression Datasets

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 effectiveness of chemical compounds as inhibitors. The main challenge in ML is design that produces high prediction accuracy so properties material can be predicted accurately. In this study, we examine performance polynomial functions ML-based NuSVR algorithm evaluating regression dataset inhibition efficiency pyridine-quinoline compounds.Methods: Polynomial algorithm-based ML.Result: outcomes demonstrate model's ability greatly enhanced by application functions. Originality: combination deep machine algorithms increase predictive models.