Ivan Palomares Carrascosa has a list:
In regression models, failure occurs when the model produces inaccurate predictions — that is, when error metrics like MAE or RMSE are high — or when the model, once deployed, fails to generalize well to new data that differs from the examples it was trained or tested on. While model failure typically shows up in one or both of these forms, the root causes can be more diverse and subtle.
This article explores some common reasons why regression models may underperform and outlines how to detect these issues. It is also accompanied by practical code excerpts using XGBoost — a robust and highly tunable ensemble-based regression model. Despite its popularity and power, XGBoost can also fail if not trained or evaluated properly!
These are high-level reasons but they’re good to keep in mind.