What is the consequence of a model that is overfitting?

An overfitting model is one that has learned the training data too well, capturing noise and outliers rather than just the underlying patterns. This leads to a situation where while the model performs exceptionally well on the training data—often achieving very low training error—it struggles to perform effectively on new or unseen data, such as the test dataset. This disparity in performance is a hallmark of overfitting and reflects the model's inability to generalize beyond the specifics of the training set.

In contrast, other options do not accurately reflect the consequences of overfitting. For instance, improving understanding of data distribution or generalizing well to new data does not take place with an overfitting model; instead, the model becomes too tailored to the training data. Additionally, running faster on smaller datasets is not inherently linked to overfitting, as the model's speed is more dependent on the complexity of the algorithms used rather than how well it fits the data.