In machine learning, what does "overfitting" refer to?

Overfitting is a critical concept in machine learning that occurs when a model learns not only the underlying patterns in training data but also the noise and outliers to an excessive degree. When a model is overfitted, it performs exceptionally well on the training data because it has effectively memorized it, capturing all its intricacies. However, this leads to poor generalization performance on unseen data because the model fails to recognize the actual patterns and relationships that apply to broader data sets outside of the training samples.

This inability to perform well on new data sets is a primary characteristic of overfitting. The model loses its ability to make accurate predictions when it encounters different data inputs that were not part of its training set, making it a critical challenge to address during model development. Techniques such as regularization, cross-validation, and employing simpler models are often utilized to mitigate overfitting and enhance the model's generalization capabilities.

The other options do not accurately capture the essence of overfitting. For instance, a model that predicts well for all data sets indicates good generalization, which is the opposite of overfitting. Additionally, while a longer training time may contribute to overfitting in certain situations, it is not a defining feature of the phenomenon