---
title: "Underfitting"
url: "https://mahouse.world/glossary/underfitting"
type: "glossary term"
topic: "Training"
author: "Ihor Vlasov"
reviewed: "2026-09-20"
published: "2026-08-10"
---

# Underfitting

Underfitting is a model too simple, too constrained or too briefly trained to capture the structure in its data, so that it performs poorly on the training set and on unseen data alike — the opposite failure to overfitting, where training performance is high and generalisation poor. Its signs are a training error that stays high and a small training–validation gap; its causes are too few parameters, too much regularisation, too few steps, or features that miss the signal.

**Also known as:** Underfit, High bias, Model too simple

## Key facts

- **Used at:** model development, where the training curve is read for both failures; and model review, where a model that underfits misses risk the data could have revealed
- **Binding:** none; but a credit or fraud model that underfits leaves detectable risk undetected, which a reviewer will call a weakness in the same way as an overfitted one
- **Typical signs:** high training loss, training and validation metrics close together and both poor, a learning curve that has flattened at an unsatisfactory level, residuals with visible structure (the model systematically misses a segment)
- **Typical causes:** a linear model for a non-linear relation, trees too shallow, a network too small, regularisation too strong, early stopping too early, a learning rate too low, insufficient epochs, or features that do not encode what matters
- **Typical remedies:** add capacity (depth, units, trees), reduce regularisation, train longer, engineer or add features, or change model class; then re-check for overfitting, which is what appears when capacity is added without care
- **Parties:** data scientists reading learning curves; reviewers asking whether the model is as good as the data allows

## How underfitting works in practice

A fraud model built as a logistic regression on 30 hand-picked features scores an AUC of 0.78 on training and 0.77 on validation. The gap is small — no overfitting — but a gradient-boosted model on the same features scores 0.88 on both; the regression could not represent the interactions (this merchant category at this hour on this device type) that the trees find. The regression underfit: not because it memorised noise but because it could not express the pattern. Adding capacity closed the gap; adding capacity carelessly (depth 15, no regularisation) then produced overfitting, and the tuned model at depth 6 landed at 0.90 validation. Reading the two failures together is the daily work: the learning curve says which side of the optimum the model sits on.

Underfitting hides more easily than overfitting because it looks stable. A model whose training and validation scores agree is reassuring; if both are mediocre, the question is whether the data could support better. The diagnostic is to try a more flexible model, or a larger one, or more features, and see whether validation improves. Features are the usual answer in tabular finance: a model without velocity features cannot detect velocity fraud however deep its trees, and a credit model without affordability features underfits the affordability risk. In deep learning the common causes are a learning rate too low, training stopped too soon, or regularisation copied from a larger dataset's recipe.

For a fintech, underfitting is the quieter of the two errors and the costlier when it hides: the fraud not caught and the default not predicted were in the data, and the model was too simple to see them.

## Example

A lender's affordability model, a scorecard with 12 binned variables built to be explainable, reaches a Gini of 0.41 on both development and validation. The model-risk team notes the agreement and passes it. A challenger built by a new analyst — gradient boosting on 60 features including bureau-derived affordability ratios — reaches 0.58 on validation and 0.57 out of time. The scorecard was underfitting: too few, too coarse variables for the relation in the data. The lender adopts the challenger with SHAP-based reason codes to preserve explainability, and the incremental prediction on a €300 million book is worth about €2.1 million a year in avoided losses at constant approval rates — risk that had been in the data all along.

## Common mistakes

- Reading a small training–validation gap as proof the model is good; it may be equally bad on both.
- Regularisation copied from another problem and never swept.
- Stopping training when the loss is still falling.
- A linear model for a relation the data shows is not.
- Fixing underfitting by adding capacity without re-checking for overfitting.

## Related terms

- [Overfitting](https://mahouse.world/glossary/overfitting.md)
- [Regularisation](https://mahouse.world/glossary/regularisation.md)
- [Hyperparameter Tuning](https://mahouse.world/glossary/hyperparameter-tuning.md)
- [Validation Data](https://mahouse.world/glossary/validation-data.md)
- [Double Descent](https://mahouse.world/glossary/double-descent.md)
- [Loss Function](https://mahouse.world/glossary/loss-function.md)

— Ihor Vlasov, M&A FinTech Expert. Last reviewed 2026-09-20. https://mahouse.world/authors/ihor-vlasov