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Overfitting indicates that a machine learning model is too complex, effectively capturing not only the underlying patterns in the training data but also the noise or random fluctuations present in that data. This results in a model that performs exceptionally well on the training dataset—often achieving high accuracy or low error rates—while failing to generalize effectively to new, unseen data. As a consequence, when evaluated on a testing dataset, the overfit model typically experiences a significant drop in performance.

This distinction is crucial because the goal of a machine learning model is to generalize well to new data, not just to memorize the training data. Therefore, while the model may exhibit impressive metrics during training, its inability to perform similarly on unseen data is a direct consequence of overfitting caused by excessive complexity, such as having too many parameters relative to the amount of training data available.