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| Дрво одлучивања× | Slučajna šuma× | |
|---|---|---|
| Oblast | Mašinsko učenje | Mašinsko učenje |
| Porodica | Machine learning | Machine learning |
| Godina nastanka≠ | 1984 | 2001 |
| Tvorac≠ | Breiman, Friedman, Olshen & Stone | Breiman, L. |
| Tip≠ | Recursive partitioning (if-then rules) | Ensemble (bagging of decision trees) |
| Temeljni izvor≠ | 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 ↗ |
| Drugi nazivi≠ | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Srodne≠ | 5 | 4 |
| Sažetak≠ | 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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