مقایسهٔ روشها
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| درخت تصمیم گروهی (Ensemble Decision Tree)× | درخت تصمیم× | درختان اضافی (Extra Trees)× | |
|---|---|---|---|
| حوزه | یادگیری ماشین | یادگیری ماشین | یادگیری ماشین |
| خانواده | Machine learning | Machine learning | Machine learning |
| سال پیدایش≠ | 1996–2000 | 1984 | 2006 |
| پدیدآور≠ | Breiman, L.; Dietterich, T. G. | Breiman, Friedman, Olshen & Stone | Geurts, P.; Ernst, D.; Wehenkel, L. |
| نوع≠ | Ensemble (multiple decision trees combined) | Recursive partitioning (if-then rules) | Ensemble (extremely randomized decision trees) |
| منبع بنیادین≠ | Dietterich, T. G. (2000). Ensemble methods in machine learning. In Multiple Classifier Systems, Lecture Notes in Computer Science, vol. 1857, pp. 1–15. Springer, Berlin, Heidelberg. DOI ↗ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ | Geurts, P., Ernst, D. & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42. DOI ↗ |
| نامهای دیگر≠ | decision tree ensemble, ensemble of decision trees, combined decision trees, multiple classifier system (decision trees) | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree | Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET |
| مرتبط≠ | 6 | 5 | 5 |
| خلاصه≠ | Ensemble Decision Tree methods train multiple decision trees and combine their outputs to produce predictions that are more accurate and stable than any single tree. Covering strategies such as bagging, random subspacing, and voting, they are among the most effective off-the-shelf techniques for tabular classification and regression tasks. | A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf. | 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. |
| ScholarGateمجموعهداده ↗ |
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