In machine learning, what does the term 'overfitting' mean?

The term 'overfitting' refers to a scenario in machine learning where a model learns the details and noise in the training data to the extent that it negatively impacts the performance of the model on new, unseen data. This typically means that while the model may exhibit excellent accuracy or performance metrics on the training dataset, it struggles to generalize to unfamiliar data, resulting in poor predictive capability when faced with real-world scenarios. Overfitting occurs because the model becomes too complex, capturing patterns that do not actually exist in the larger population, and thus fails to make accurate predictions on other datasets.

In this context, the other mentioned options describe scenarios that do not accurately capture the essence of overfitting. The inability to learn from training data implies a lack of model capacity rather than excessive fitting to the data. The consideration of using too many features might lead to overfitting as a consequence but does not define it outright. Lastly, a model being unable to run due to resource constraints is unrelated to the concept of overfitting and speaks more to operational or computational issues rather than the model's ability to generalize.