Bandingkan metode

	Extra Trees yang Dapat Dijelaskan	Pohon Ekstra
Bidang	Pembelajaran Mesin	Pembelajaran Mesin
Keluarga	Machine learning	Machine learning
Tahun asal	2006 (Extra Trees); 2017 (SHAP integration)	2006
Pencetus	Geurts, P., Ernst, D., Wehenkel, L. (Extra Trees); Lundberg, S. M. (SHAP explainability layer)	Geurts, P.; Ernst, D.; Wehenkel, L.
Tipe	Ensemble (randomized trees) with post-hoc explainability	Ensemble (extremely randomized decision trees)
Sumber perintis	Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42.	Geurts, P., Ernst, D. & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42.
Alias	XAI-ET, Explainable ET, Interpretable Extra Trees, Extra Trees with SHAP	Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET
Terkait	5	5
Ringkasan	Explainable Extra Trees combines the Extremely Randomized Trees (Extra Trees) ensemble algorithm with post-hoc explainability methods — most commonly SHAP values — to deliver both strong predictive performance and transparent, feature-level explanations. It extends the classic Extra Trees classifier or regressor so that every prediction can be decomposed into individual feature contributions, satisfying demands for accountability in applied and regulated domains.	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.