Analyzing the Effect of Model Overfitting on Predictive Accuracy and Statistical Generalization Performance

Posted: Oct 21, 2021

• Noah Rivera
• Sophia Hill
• Matthew Jones

Abstract

The phenomenon of overfitting represents one of the most fundamental challenges in machine learning and statistical modeling. Traditional understanding characterizes overfitting as occurring when a model learns the training data too well, including its noise and random fluctuations, thereby compromising its ability to generalize to unseen data. This conventional wisdom has guided decades of machine learning practice, leading to the widespread adoption of regularization techniques, early stopping, and model complexity controls. However, recent empirical observations and theoretical developments have begun to challenge this monolithic view of overfitting, suggesting that the relationship between model complexity, training performance, and generalization may be more nuanced than previously recognized. Our research addresses critical gaps in the current understanding of overfitting by systematically investigating its dual impact on predictive accuracy and statistical generalization performance. We propose that overfitting should not be viewed as a binary condition but rather as a spectrum of behaviors with varying implications for model performance. This perspective enables us to identify circumstances under which increased model complexity—traditionally associated with overfitting—can paradoxically enhance both training and test performance, a phenomenon we term 'beneficial overfitting.'