Translation: Underfitting occurs when a model is too simple to capture the important patterns in the training data. It performs poorly on both the training data and new data because it fails to learn the underlying patterns. Underfitting can be mitigated by increasing model complexity, incorporating more informative features, or applying fine tuning.