What characterizes an overfitting model?

An overfitting model is characterized by its poor performance on new or unseen data. This issue arises when a model learns not only the underlying patterns from the training data but also the noise and fluctuations that are specific to that data set. As a result, while the model may demonstrate high accuracy on the training set, it fails to generalize its learning to new data, exhibiting a significant drop in performance when faced with inputs that were not included during its training phase.

The phenomenon of overfitting highlights the importance of balancing model complexity and generalization capabilities. A model that is too complex may capture all the nuances of the training data, while a model that generalizes well maintains a sufficient level of complexity to learn from the data without being overly tailored to it. This characteristic fundamentally differentiates how well a model can perform in real-world scenarios, where it is often tested against unseen data.