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# Model optimisation

Model optimisation uses explanation outputs to improve how a model is configured, trained, and reviewed.

Explanations can help identify which features and modules matter most, which ones matter least, and whether those rankings align with real-world knowledge, expectations, and constraints. This can inform both feature selection and the module creation process during [induction](../induction).

## Optimising with explanations

After a model has been induced, [global explanations](../explanations/global-explanations/overview) are often used to understand how the model behaves.

Two of the most useful starting points are [feature importance and module importance](../explanations/global-explanations/module-and-feature-importance).

These explanations rank and score:

| Explanation        | What it ranks                                                   |
| ------------------ | --------------------------------------------------------------- |
| Feature importance | Input features, using decomposed attribution values.            |
| Module importance  | XNN modules, including feature and feature-interaction modules. |

These rankings help identify which parts of the model have the largest and smallest impact on predictions.

## Feature selection

Feature importance can help identify low-ranking features.

A low-ranking feature may be a candidate for removal from the training dataset, especially when its low importance aligns with domain knowledge, business expectations, or known constraints.

This should be reviewed carefully. A feature may rank low globally while still being important for a specific segment, interaction, or edge case.

## Module creation

Module importance can help identify low-ranking modules.

A low-ranking module may be a candidate for exclusion from the module creation process during [induction](../induction), especially if the module represents a feature or interaction that is not expected to be useful.

This can help simplify the model and focus induction on structures that are more likely to be meaningful.

## Comparing explanations with domain knowledge

Importance rankings should be compared with real-world knowledge, expectations, and constraints.

If a feature or module is highly important but should not matter according to domain knowledge, it may require investigation. If a feature or module is consistently low-ranking and has no expected relevance, it may be safe to remove or exclude.

This comparison helps ensure model optimisation remains grounded in both model evidence and domain understanding.