Underfitting occurs when a model is insufficiently expressive to capture the underlying patterns in the training data. This situation is identified when both the training error and validation error are substantial, but the generalization gap ($$R_	extrm{emp} - R$$) between them remains small. Because the model fails to reduce the training error despite a small gap, overall predictive performance could likely be improved by utilizing a more complex model.

Underfitting

Which of the following methods can prevent overfitting?

Prevent Overfitting

https://elitedatascience.com/overfitting-in-machine-learning
Overfitting in Machine Learning: What It Is and How to Prevent It

University of Michigan - Ann Arbor

1. Cross-validation
2. Train with more data.
3. Remove features.
4. Early stopping.
5. Regularization.
6. Ensembling.

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