A Theoretical Perspective and Experimental Evaluation of the Extensive Analysis of Loss Functions in Machine Learning and Deep Learning

Machine learning and deep learning have revolutionized various fields, including image classification, regression, and probabilistic modeling. A key component in these algorithms is the choice of loss function, which significantly influences the performance of models across different tasks. This study aims to evaluate various loss functions in terms of overall accuracy for image classification and error generation in regression tasks, combining theoretical discussions and experimental research. This paper compares eleven popular loss functions from three different categories: mean-squared logarithmic error, kl-divergence, Huber loss, hinge loss, mean-absolute error, mean absolute percentage error, log-cosh loss, mean absolute percentage error, and sparse categorical cross-entropy. It analyses the extent to which these functions generate errors and perform overall through theoretical discussion and experimental evaluation of image classification and regression problems. The experimental setup is then described, including the datasets used (TensorFlow Flower dataset for image classification and Combined Cycle Power Plant dataset for regression), hardware and software details, and hyperparameter settings. The paper compares the performance of these loss functions across different datasets, providing a comprehensive analysis of their strengths and weaknesses. This research expands the knowledge of loss function selection in machine learning and deep learning, which will benefit researchers and developers alike in optimizing model outputs across a range of applications.