How do you determine if a model is underfitted or overfitted?

To understand if a model is underfitted or overfitted, analyze the error behavior on training and validation sets:

• Underfitting: The model performs poorly on both training and test sets — high error everywhere. This means the model is too simple and cannot capture the underlying patterns.

• Overfitting: The model shows very low error on the training set but high error on the test set. The model has memorized the training data, including noise, and does not generalize well.

To diagnose, it is common to plot the error against model complexity or number of training epochs. The ideal model has low error on both sets.

Example:

Model	Error on training	Error on test	Conclusion
Simple	High	High	Underfitting
Complex	Low	High	Overfitting
Optimal	Low	Low	Good model

To combat overfitting, regularization, more data, simpler models, or early stopping methods are used.