What is model overfitting?

Model overfitting occurs when a machine learning model learns the intricate details and noise in the training data to an extent that it negatively impacts its performance on new, unseen data. In other words, the model becomes too complex and specific to the training dataset, capturing not just the underlying trends but also the random fluctuations that do not generalize to other datasets.

This results in a high accuracy on the training data but poor performance when applied to test data or real-world inputs, where those specific patterns do not exist. Hence, the correct option highlights that the model is overly tuned to the training dataset, capturing both the legitimate patterns and the inconsistencies or noise. This is a key challenge in machine learning, as the goal is to create a model that balances learning rich features while still being able to generalize effectively.