Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Ekstra puud× | Otsustuspuu× | |
|---|---|---|
| Valdkond | Masinõpe | Masinõpe |
| Perekond | Machine learning | Machine learning |
| Tekkeaasta≠ | 2006 | 1984 |
| Looja≠ | Geurts, P.; Ernst, D.; Wehenkel, L. | Breiman, Friedman, Olshen & Stone |
| Tüüp≠ | Ensemble (extremely randomized decision trees) | Recursive partitioning (if-then rules) |
| Algallikas≠ | Geurts, P., Ernst, D. & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42. DOI ↗ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ |
| Rööpnimetused≠ | Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree |
| Seotud | 5 | 5 |
| Kokkuvõte≠ | 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. | 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. |
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