Which of the following describes a model trained with excessive bias?

A model trained with excessive bias exhibits consistent errors in its predictions, which is indicative of the model's inability to generalize well to new, unseen data. This stems from the model's overly simplistic assumptions about the underlying data distribution. When a model is biased, it tends to miss relevant relations between features and target outputs, leading to systematic errors in its predictions regardless of the input it receives.

In contrast, a model that performs well on training data but poorly on unseen data tends to indicate high variance rather than excessive bias; it suggests that the model has overfit to the noise in the training data instead of capturing the underlying patterns. An overly complex model is typically associated with high variance as well, which means it can adapt too closely to the training data but fails to perform well on independent test sets. Lastly, requiring extensive training data to perform accurately does not directly relate to bias; it more often relates to the model's complexity or its capacity to learn effectively from available data. This option suggests issues related to data size or quality rather than bias itself.