Extra Trees
Extremely randomized ensemble regressor with fast training
Extra Trees Regressor builds an ensemble using random split thresholds, making it faster than Random Forest while achieving similar accuracy and variance reduction.
When to use:
- Large datasets where Random Forest is too slow
- When additional randomization helps reduce overfitting
- General-purpose regression with ensemble stability
Input: Tabular data with the feature columns defined during training Output: Continuous predicted value
Model Settings (set during training, used at inference)
N Estimators (default: 100) Number of trees in the ensemble.
Max Depth (default: null — unlimited) Maximum tree depth.
Max Features (default: 1.0) Features considered per split.
Min Samples Leaf (default: 1) Minimum samples per leaf.
Inference Settings
No dedicated inference-time settings.