Exploring Optimization Strategies for Support Vector Machine -Based Half Cell Potential Prediction
Yogesh Iyer Murthy,
Shikha Pandey,
Sumit Gandhi
Abstract:Purpose
This study aims to evaluate the predictive performance of Support Vector Machine (SVM) models in estimating HCP values based on input parameters, employing Bayesian Optimization, Grid Search, and Random Search optimization techniques.
Study Design/Methodology
Using a dataset containing 1134 rows and six columns, Principal Component Analysis (PCA) is utilized to reduce dimensionality while preserving 95% of the explained variance. Input parameters such as temperature, age, relative humidity, and X and Y… Show more
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