Bandingkan metode
Tinjau metode pilihan Anda berdampingan; baris yang berbeda akan disorot.
| Pohon Keputusan× | Random Forest× | |
|---|---|---|
| Bidang | Pembelajaran Mesin | Pembelajaran Mesin |
| Keluarga | Machine learning | Machine learning |
| Tahun asal≠ | 1984 | 2001 |
| Pencetus≠ | Breiman, Friedman, Olshen & Stone | Breiman, L. |
| Tipe≠ | Recursive partitioning (if-then rules) | Ensemble (bagging of decision trees) |
| Sumber perintis≠ | 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 ↗ |
| Alias≠ | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Terkait≠ | 5 | 4 |
| Ringkasan≠ | 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. |
| ScholarGateSet data ↗ |
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