RandomForest 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).