What does "overfitting" refer to in machine learning?

Overfitting in machine learning specifically refers to a model that is so closely fitted to the training data that it captures not only the underlying patterns but also the noise and fluctuations present in that data. This means that while the model performs exceptionally well on the training dataset, it typically fails to generalize to new, unseen data, resulting in poor performance during validation or testing phases.

Capturing noise signifies that the model has learned irrelevant details that do not contribute to its predictive power, making it less effective in real-world scenarios where data might differ from the training set. Thus, the concept of overfitting is crucial for understanding the balance needed in model complexity and generalization during the training process. Recognizing this helps guide decisions on regularization techniques or cross-validation to avoid such pitfalls during model development.