Training & Fine-tuning

Underfitting

When a model hasn't learned enough to perform well on its task.

Definition

Underfitting is the opposite of overfitting — the model hasn't captured enough of the pattern in the training data to perform well even on familiar examples. This can happen when the model is too simple for the task, the training data is insufficient, or training is stopped too early. An underfitted model gives poor results consistently, whereas an overfitted model gives good results in testing but poor results in deployment.

Related Terms

Overfitting

When a model memorises training data rather than learning general patterns.

Machine Learning (ML)

A method where computers learn patterns from data rather than being explicitly programmed.

Training Data

The text, documents, or other content an AI learns from during its development.

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Disclaimer

This definition is provided for educational and informational purposes only. It represents a general explanation of a technical concept and does not constitute professional, technical, or investment advice. Artificial intelligence is a rapidly evolving field; terminology, techniques, and capabilities change frequently. Coaley Peak Ltd makes no warranty as to the accuracy, completeness, or currency of the information provided. Nothing on this page should be relied upon as the sole basis for commercial, technical, legal, or investment decisions without independent professional advice.

Document reference: ISO_webpage_knowledge-base_glossary_v1

Last modified: 29 March 2026

Knowledge Base·Training & Fine-tuning·Underfitting