Methoden vergleichen
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| Bagging (Bootstrap Aggregating)× | Entscheidungsbaum× | Random Forest× | |
|---|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning | Machine learning |
| Entstehungsjahr≠ | 1996 | 1984 | 2001 |
| Urheber≠ | Breiman, L. | Breiman, Friedman, Olshen & Stone | Breiman, L. |
| Typ≠ | Ensemble meta-algorithm (variance reduction via bootstrap aggregation) | Recursive partitioning (if-then rules) | Ensemble (bagging of decision trees) |
| Wegweisende Quelle≠ | Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140. DOI ↗ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Aliasnamen≠ | Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Verwandt≠ | 5 | 5 | 4 |
| Zusammenfassung≠ | Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner. | 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. | 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. |
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