Models systematically underestimate values on the test set, why? I used different models to train on the test set and to predict on the test set. The commonality is that all models underestimate the true values on the test set. Which steps should I take to understand why?
Welcome to data science SO. It seems your model has a bias towards a lower estimate of the true values. This can happen for a variety of reasons. Say you are trying to fit a straight line on data that was generated by a second degree polynomial. The straight line fit will have a bias. Typically bias happens because the model you are trying to build is insufficient able to capture the dynamics of the data you are trying to fit it on. I understand this is very generic but without more info about what model you used or any plot of the test set it is hard to help you more.