Evaluation Metrics for Machine Learning Techniques: A Comprehensive Review and Comparative Analysis of Performance Measurement Approaches

Seyed-Ali Sadegh-Zadeh1 Kaveh Kavianpour2 Hamed Atashbar3 Elham Heidari4 Saeed Shiry Ghidary5 Mozafar Saadat6

چکیده :
Abstract: Evaluation metrics play a critical role in assessing the performance of machine learning models. In this review paper, we provide a comprehensive overview of performance measurement approaches for machine learning models. For each category, we discuss the most widely used metrics, including their mathematical formulations and interpretation. Additionally, we provide a comparative analysis of performance measurement approaches for metric combinations. Our review paper aims to provide researchers and practitioners with a better understanding of performance measurement approaches and to aid in the selection of appropriate evaluation metrics for their specific applications.

کلمات کلیدی : evaluation metrics performance measurement supervised learning unsupervised learning reinforcement learning model robustness and stability comparative analysis


زبان مقاله : فارسی
محور مقاله : مهندسی کامپیوتر
محل انتشار : اولین کنفرانس بین المللی رویکردهای نوین در مهندسی و علوم پایه
آدرس وب سایت کنفرانس : icnabs.ir/1st
کد IOI مقاله : XKGC-AZAZKE
لینک مستقیم مقاله : https://isnac.ir/XKGC-AZAZKE