#### Monotonic feature constraints {: #monotonic-feature-constraints }

**Monotonic constraints** control the influence, both up and down, between variables and the target. In some use cases (typically insurance and banking), you may want to force the directional relationship between a feature and the target (for example, higher home values should always lead to higher home insurance rates). By training with monotonic constraints, you force certain XGBoost models to learn only monotonic (always increasing or always decreasing) relationships between specific features and the target. 

![](images/wb-exp-34.png)

Using the monotonic constraints feature requires [creating special feature lists](monotonic), which are then selected here. Note also that when using Manual mode, available blueprints are marked with a MONO badge to identify supporting models.

#### Weight {: #weight }

**Weight** sets a single feature to use as a differential weight, indicating the relative importance of each row. It is used when building or scoring a model—for computing metrics on the Leaderboard—but not for making predictions on new data. All values for the selected feature must be greater than 0. DataRobot runs validation and ensures the selected feature contains only supported values.

![](images/wb-exp-35.png)

