Ivan Palomares Carrascosa looks into a poorly-fitting model:
In classification models, failure occurs when the model assigns the wrong class to a new data observation; that is, when its classification accuracy is not high enough over a certain number of predictions. It also manifests when a trained classifier fails to generalize well to new data that differs from the examples it was trained on. While model failure typically presents itself in several forms, including the aforementioned ones, the root causes can sometimes be more diverse and subtle.
This article explores some common reasons why classification models may underperform and outlines how to detect, diagnose, and mitigate these issues.
The explanations are fairly high-level and focus mostly on two-class rather than multi-class classification, but there is some good guidance in here.