Underfitting

Underfitting occurs when a model is too simple or insufficiently trained to capture important patterns in the data. The technique fits into the broader machine-learning lifecycle that includes data preparation, training, evaluation, deployment, and monitoring. It also gives teams a clearer basis for comparing implementation options and deciding where the concept fits in a production system.