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> ML_ALGORITHM // EXTRA-TREES_v1.0

Extremely Randomized Trees (Extra Trees)

Tree ensemble that samples random cut-points for each feature instead of computing the optimal split threshold, accelerating training while reducing variance.

Tree & Rule Ensemblesclassical-supervisedmoderate-posthocmedium (1k-100k)
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Computational Complexity
Training Complexity:Faster than Random Forest: O(n_trees * k * n)
Inference Complexity:O(n_trees * depth)
Hardware Profile
CPU Friendly:Yes
Requires GPU:No
Memory Footprint:moderate
Interpretability & Data
Interpretability Tier:moderate-posthoc
Training Data Needs:medium (1k-100k)

Interpretability Assessment

Ensemble evaluated via feature importances and TreeSHAP.

Suitable Tasks & Supported Modalities

Suitable Tasks:
binary classificationmulticlass classificationregression
Supported Modalities:
tabular

Implementing Libraries

scikit-learnscikit-learn Consortium / Inria · v1.5.2
View Spec
caret-r

Foundational Literature

Common Pitfalls & Warnings
  • Increased bias on highly structured, low-dimensional datasets