Corporate Governance and AI Ethics of Classification Methods Using Supervised Machine Learning for Decsion Making
Anurag Shrivastava, RVS Praveen, Aboothar Mahmood Shakir, Apurv Verma, Kanchan Yadav, P. William
Abstract
In this extensive study, a broad variety of supervised machine learning classification methods in intelligent environments are compared and contrasted with one another. A number of different algorithms, including Decision Trees, k-Nearest Neighbors, Support Vector Machines, and Artificial Neural Networks, are evaluated based on their efficacy via the use of ROC curves and precision-recall measurements. The study highlights the important trade-offs that need to be made between the complexity of the model, the efficiency of the computation, and the accuracy of the results. By paying careful attention to the ways in which algorithmic performance and dataset attributes interact with one another, the findings of the research provide insight on which classification approach is best suited for a particular set of conditions. The purpose of the research is to provide practitioners with assistance in selecting the most appropriate classification algorithms for applications using big data and the Internet of Things in smart environments.