« Back to Glossary Index

The phenomenon where a Machine Learning algorithm fails to capture the underlying structure of the data, usually because the model is too simple or not suitable for the task. This results in poor performance, as the model cannot generalize well to the data. Underfitting is the opposite of overfitting.