Bandingkan kaedah

	Ensembel Undian	Pokok Tambahan	Random Forest
Bidang	Pembelajaran Mesin	Pembelajaran Mesin	Pembelajaran Mesin
Keluarga	Machine learning	Machine learning	Machine learning
Tahun asal	1990s–2004	2006	2001
Pengasas	Lam & Suen; Kuncheva, L. I. (systematic treatment)	Geurts, P.; Ernst, D.; Wehenkel, L.	Breiman, L.
Jenis	Ensemble (combination of multiple classifiers by vote)	Ensemble (extremely randomized decision trees)	Ensemble (bagging of decision trees)
Sumber perintis	Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8	Geurts, P., Ernst, D. & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Alias	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble	Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Berkaitan	5	5	4
Ringkasan	A voting ensemble trains several diverse classifiers independently and combines their predictions by a vote: hard voting picks the class chosen by the most models, while soft voting averages their class-probability estimates, optionally with per-model weights. The combination usually outperforms any individual member, and requires no additional training after the base models are fitted.	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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.