Extra Trees
Extremely Randomized Trees (Extra-Trees) · Also known as: Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET
Extra Trees (Extremely Randomized Trees), introduced by Geurts, Ernst, and Wehenkel in 2006, is an ensemble of decision trees that pushes randomisation further than Random Forest. Both the candidate features and the split thresholds are chosen completely at random at each node, eliminating the greedy search over thresholds. This extra randomness reduces variance, often matches or exceeds Random Forest accuracy, and runs substantially faster at training time.
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When to use it
Use Extra Trees when you want fast training and strong accuracy on tabular data with continuous or mixed-type features. It is particularly useful when training time is a constraint — its removal of threshold optimisation makes it considerably faster than Random Forest at comparable accuracy. It suits classification and regression tasks with moderate to large samples (roughly 50 or more observations). Prefer Random Forest or gradient boosting if you need built-in out-of-bag estimates or if dataset noise is very high and you suspect fully random thresholds hurt accuracy. For very small datasets, simpler models such as logistic regression or a single decision tree are safer and more interpretable.
Strengths & limitations
- Faster training than Random Forest because no threshold optimisation is performed at each split.
- High diversity among trees leads to strong variance reduction and often matches or beats Random Forest accuracy.
- Handles mixed feature types and nonlinear interactions without preprocessing assumptions.
- Provides native feature importances for variable ranking and selection.
- Scales well to high-dimensional data with many irrelevant features.
- No built-in out-of-bag estimate (unlike Random Forest), so cross-validation is required for unbiased error assessment.
- Fully random thresholds can hurt on very noisy data where optimal splits are informative.
- Black-box: provides no explicit coefficients, limiting interpretability for audiences expecting effect sizes.
- Memory usage grows with the number of trees and tree depth.
Frequently asked
How does Extra Trees differ from Random Forest?
Both are ensembles of decision trees with random feature subsets at each split. The key difference is that Extra Trees picks split thresholds completely at random rather than optimising them, and by default trains on the full training set rather than bootstrap samples. This makes Extra Trees faster and typically produces more diverse, less correlated trees.
Which should I prefer — Extra Trees or Random Forest?
When training speed matters or the data are relatively clean, Extra Trees often matches or exceeds Random Forest accuracy at lower computational cost. If you want a built-in out-of-bag estimate or your data are noisy (where better split thresholds help), Random Forest is safer.
How many trees do I need?
Start with 100 trees and increase until performance stabilises on a validation set or in cross-validation. More trees improve stability but increase memory and prediction latency.
Does Extra Trees overfit?
Individual trees are grown to full depth and can overfit in isolation, but the ensemble average strongly reduces variance. Overfitting of the full ensemble is generally rare, though on very small datasets with many features it can still occur.
How do I assess model performance without an OOB set?
Use k-fold cross-validation — typically 5 or 10 folds. Evaluate classification tasks with accuracy, F1, and AUC; evaluate regression tasks with RMSE and R-squared.
Sources
- Geurts, P., Ernst, D. & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42. DOI: 10.1007/s10994-006-6226-1 ↗
- Extra-Trees. Wikipedia. link ↗
How to cite this page
ScholarGate. (2026, June 3). Extremely Randomized Trees (Extra-Trees). ScholarGate. https://scholargate.app/en/machine-learning/extra-trees
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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