A Novel Outlier-Robust Accuracy Measure for Machine Learning Regression Using a Non-Convex Distance Metric

Ahmad B. Hassanat (), Mohammad Khaled Alqaralleh, Ahmad S. Tarawneh (), Khalid Almohammadi, Maha Alamri, Abdulkareem Alzahrani, Ghada A. Altarawneh and Rania Alhalaseh
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Ahmad B. Hassanat: Faculty of Information Technology, Mutah University, Karak 61710, Jordan
Mohammad Khaled Alqaralleh: Faculty of Information Technology, Mutah University, Karak 61710, Jordan
Ahmad S. Tarawneh: Faculty of Information Technology, Mutah University, Karak 61710, Jordan
Khalid Almohammadi: Department of Computer Science, Applied College, University of Tabuk, Tabuk 47512, Saudi Arabia
Maha Alamri: Department of Systems and Networking, Faculty of Computing and Information, Al-Baha University, Al-Baha 65779, Saudi Arabia
Abdulkareem Alzahrani: Department of Computer Science, Faculty of Computing and Information, Al-Baha University, Al-Baha 65779, Saudi Arabia
Ghada A. Altarawneh: Faculty of Business, Mutah University, Karak 61710, Jordan
Rania Alhalaseh: Faculty of Information Technology, Mutah University, Karak 61710, Jordan

Mathematics, 2024, vol. 12, issue 22, 1-20

Abstract: Regression, a supervised machine learning approach, establishes relationships between independent variables and a continuous dependent variable. It is widely applied in areas like price prediction and time series forecasting. The performance of regression models is typically assessed using error metrics such as the Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). However, these metrics present challenges including sensitivity to outliers (notably MSE and RMSE) and scale dependency, which complicates comparisons across different models. Additionally, traditional metrics sometimes yield values that are difficult to interpret across various problems. Consequently, there is a need for a metric that consistently reflects regression model performance, independent of the problem domain, data scale, and outlier presence. To overcome these shortcomings, this paper introduces a new regression accuracy measure based on the Hassanat distance, a non-convex distance metric. This measure is not only invariant to outliers but also easy to interpret as it provides an accuracy-like value that ranges from 0 to 1 (or 0–100%). We validate the proposed metric against traditional measures across multiple benchmarks, demonstrating its robustness under various model scenarios and data types. Hence, we suggest it as a new standard for assessing regression models’ accuracy.

Keywords: regression; machine learning; performance assessment; Hassanat distance (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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