What does 'overfitting' refer to in model training?

Overfitting occurs when a model learns not only the underlying patterns in the training data but also the noise and random fluctuations present in that data. This typically happens when a model is too complex relative to the amount of training data available. As a result, the model performs exceptionally well on the training dataset, but this performance does not translate to new, unseen data. Instead of generalizing and making accurate predictions on new inputs, an overfitted model is likely to make inaccurate predictions because it has essentially memorized the training data rather than understanding the underlying relationships. Therefore, option B accurately describes overfitting as it highlights the model's tendency to learn noise along with the true signals in the training dataset.