Systematic Optimization of Overfitting Problem in Machine Learning

Abstract

The significant challenge of models in machine learning not being able to generalize sufficiently from seen to unseen data is known as overfitting. Overfitting occurs when a model exhibits perfect accuracy on its training dataset but performs poorly on new, test datasets. This issue not only makes the model overly complex and increases computational costs, but it also leads to its instability. Overfitting can be tackled through various methods, which are extensively discussed in the present article. Specifically, the article focuses on three main strategies: regularization, ensemble learning, and data augmentation. Each of these approaches is introduced with a brief explanation of their foundational principles and techniques. Moreover, the practical impact of these strategies is illustrated through several recent case studies across different fields such as text processing, agriculture, and healthcare. These approaches have been shown to significantly enhance the accuracy of models compared to traditional training methodologies, demonstrating their effectiveness in real-world applications.