> ## Documentation Index
> Fetch the complete documentation index at: https://shapely.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# Random Forest Regressor
> Random Forest Regressor creates an ensemble of decision trees, each trained on a random subset of the data. The final prediction is the average of all tree outputs, producing robust and stable results.
Random Forest provides built-in **feature importance** rankings, helping you understand which input features contribute most to the prediction.
**When unsure which algorithm to use, Random Forest is an excellent and safe default.**
### Use Cases
* **Complex geometric relationships** in parametric design
* **Energy simulation** surrogate models
* **Cost prediction** when interactions between variables are non-linear
* **Generative design** ranking models
