International Journal of Computer
Trends and Technology

Research Article | Open Access | Download PDF

Volume 73 | Issue 7 | Year 2025 | Article Id. IJCTT-V73I7P105 | DOI : https://doi.org/10.14445/22490183/IJCTT-V73I7P105

Loss Functions in Artificial Intelligence and Machine Learning: A Comprehensive Overview

Rishi Mohan

Received Revised Accepted Published
30 May 2025 22 Jun 2025 14 Jul 2025 28 Jul 2025

Citation :

Rishi Mohan, "Loss Functions in Artificial Intelligence and Machine Learning: A Comprehensive Overview," International Journal of Computer Trends and Technology (IJCTT), vol. 73, no. 7, pp. 39-43, 2025. Crossref, https://doi.org/10.14445/22490183/IJCTT-V73I7P105

Abstract

Loss functions play a central role in machine learning by quantifying how far predicted outcomes deviate from actual values, directly influencing model optimization. This paper presents a structured and comparative overview of key loss functions used across regression and classification tasks. It evaluates regression-based losses—such as Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Huber Loss, and Log-Cosh—focusing on their sensitivity to outliers and interpretability. Classification loss functions—such as Cross-Entropy, Hinge Loss, Kullback-Leibler Divergence, and Focal Loss—are assessed for their effectiveness in probabilistic modeling and handling imbalanced datasets. Each loss function is presented with its mathematical formulation, practical examples, and trade-offs, followed by a comparative analysis to guide selection based on task requirements. A comparative table outlines their strengths, limitations, and ideal use cases. The paper not only demystifies the mathematical underpinnings of loss functions but also provides practical insights for selecting the appropriate loss mechanism across various machine learning contexts, with the goal of improving training efficiency and model accuracy. 

Keywords

Loss functions, Model optimization, Regression metrics, Classification loss, Robust learning, Deep learning, Prediction error. 

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