What does overfitting in machine learning refer to?

Overfitting in machine learning refers to a situation where a model becomes too complex and learns not only the underlying patterns in the training data but also the noise within that data. This leads to a model that performs exceptionally well on the training dataset but fails to generalize to unseen data, resulting in poor performance when it comes to making predictions on new instances.

When a model overfits, it captures specific details that do not apply broadly; these include fluctuations and anomalies that do not represent the actual trends or relationships the model should learn. This is often characterized by a significant gap between training accuracy and validation or testing accuracy—where training accuracy remains high, but testing accuracy drops. To combat overfitting, techniques like cross-validation, regularization, or pruning are utilized, aiming to simplify the model and enhance its generalization capabilities.

In contrast, the other options describe different aspects of model performance that do not address the concept of overfitting. For example, a model performing well on unseen data indicates good generalization, not overfitting. Similarly, generalization itself showcases a model effectively applying learned knowledge to new data, while needing more training data may relate to underfitting rather than overfitting.