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