> ## 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 Classifier
> Random Forest creates a collection of independent decision trees, each trained on a random subset of the data. When making a prediction, every tree votes and the most popular class wins.
This ensemble approach reduces overfitting and makes predictions more robust than any single decision tree.
* Random Forest is one of the most versatile classifiers available. It handles both numerical and categorical features, requires minimal parameter tuning, and can rank features by importance.
* When you are unsure which algorithm to use, Random Forest is an excellent default choice.
### Use Cases
* **Multi-class classification**: Classify elements into multiple categories (e.g., wall types, floor categories, room functions).
