What happens when a model exhibits high bias?

When a model exhibits high bias, it indicates that the model is simplifying the problem too much and is unable to capture the underlying patterns in the training data effectively. This simplification leads to a systematic error in predictions, as the model fails to learn from the intricacies and complexities present within the data. As a result, it can perform poorly not only on the training dataset but also on new, unseen data, ultimately resulting in low accuracy across different datasets.

High bias is often associated with underfitting, where the model is too simplistic and does not have enough capacity (like too few parameters) to learn the data well. This lack of flexibility prevents the model from accurately modeling relevant trends, thus leading to significant errors in both training and testing datasets.