What does overfitting in a machine learning model indicate?

Overfitting in a machine learning model refers to a situation where the model has learned the training data too well, capturing noise and outliers rather than just the underlying pattern. This means the model effectively memorizes the training examples, leading to extremely high accuracy on that specific dataset. However, when presented with new, unseen data, the model tends to perform poorly because it cannot generalize its learned knowledge beyond the specifics of the training set.

This is contrasted with a well-generalized model, which should perform consistently across both training and testing datasets by identifying broader patterns rather than specific details. Thus, the correct understanding of overfitting highlights the importance of balancing model complexity and training data to ensure robust performance.