Evaluation Metrics for Machine Learning Techniques: A Comprehensive Review and Comparative Analysis of Performance Measurement Approaches
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
1) Department of Computing, School of Digital, Technologies and Arts, Staffordshire University, Stoke-on-Trent ST4 2DE, UK,
2) Computer Engineering Department, Amirkabir University of Technology, 424 Hafez Ave, Tehran
3) Computer Engineering Department, Amirkabir University of Technology, 424 Hafez Ave, Tehran
4) Computer Engineering Department, Amirkabir University of Technology, 424 Hafez Ave, Tehran
5) Computer Engineering Department, Amirkabir University of Technology, 424 Hafez Ave, Tehran
6) Department of Mechanical Engineering, School of Engineering, University of Birmingham, Birmingham B15 2SQ, UK ,
محل انتشار :
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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