What condition is associated with a model that is too complex for the data it is trained on?

A model that is too complex for the data it is trained on is associated with overfitting. Overfitting occurs when a model captures noise and details in the training data to the extent that it fails to generalize well to new, unseen data.

When a model is overfitted, it performs exceptionally well on the training data, often achieving very high accuracy, but it may struggle significantly with validation data. This happens because the model learns the peculiarities and random fluctuations of the training set rather than the underlying patterns that would apply more broadly across other datasets.

In contrast, high bias typically indicates that a model is too simplistic, leading to systematic errors in predictions. This is a different issue, where the model fails to capture the true relationships in the data. High variance refers to the model’s sensitivity to fluctuations in the training data, which can also relate to overfitting but focuses more on the model's inconsistency than its complexity. Underfitting is the scenario where a model is too simple to capture the underlying trends in the data, resulting in poor performance on both training and validation datasets.