Underfitting

Underfitting occurs in machine learning when a model is too simple to capture the underlying patterns in the data. This often happens when the model does not have enough parameters or complexity to learn from the data effectively.

How Underfitting Manifests

A model that underfits usually has poor performance on both training and validation datasets. This can be due to reasons like insufficient training time, overly simple model architecture, or lack of relevant features in the data. Improving the model complexity or feature engineering often helps in addressing underfitting.