Model overrides ensure predetermined prediction outcomes based on a set of defined rules.
A single override or rule is composed of two elements:
- A set of logical conditions based on the features and/or prediction, that describe a subpopulation of rows.
- A desired outcome for the matching rows.
For classification tasks, the outcome will be the desired class.
For regression tasks, the outcome will be restricted between the min and max values specified.
Only the earliest applicable overrides in this list will be applied to any matching row.
Example: we predict car quotations and we want to enforce a certain price for old/worn-out models
![override explanation]()
![override explanation]()