Overfitted Models
An overfitted model is a machine learning model that performs exceptionally well on its training data but poorly on new, unseen data, due to learning noise and random fluctuations rather than the underlying pattern. This occurs when the model is too complex relative to the amount of training data, capturing irrelevant details that don't generalize. Overfitting is a common pitfall in machine learning that leads to poor predictive performance in real-world applications.
Developers should learn about overfitted models to avoid building ineffective machine learning systems that fail in production, as overfitting undermines model reliability and business value. Understanding this concept is crucial when working with limited data, complex models like deep neural networks, or in high-stakes domains like healthcare or finance where generalization errors can have serious consequences. It helps in applying techniques like cross-validation, regularization, and early stopping to improve model robustness.