What does a model with high bias typically suffer from?

A model with high bias is characterized by underfitting, meaning it fails to capture the underlying trends and complexities present in the data. This is often due to the model being too simplistic or having too few parameters to explain the variation in the dataset effectively. As a result, it produces predictions that are consistently off the mark, leading to poor performance on both the training set and new unseen data.

High bias arises when the model assumptions are too strong or rigid, which prevents it from adapting to the data properly. For instance, a linear regression model applied to a nonlinear relationship would illustrate high bias since it would not be flexible enough to accommodate the variations in the data.

In contrast, overfitting, unexplained variances, and a lack of features can lead to other modeling issues, but they do not correlate with the specific issue of high bias. Hence, stating that a model with high bias suffers from underfitting provides a clear understanding of the deficiencies such models typically experience.