STOP OVERFITTING WITH PROVEN TECHNIQUES

Abstract

Overfitting is a common challenge in machine learning where a model learns the noise in the training data rather than the underlying pattern. This results in excellent performance on the training data but poor generalization to new, unseen data. In this article, we delve into effective strategies and best practices to mitigate overfitting and build more robust machine learning models.