Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| AdaBoost× | Mti wa Uamuzi× | Msitu Nasibu× | |
|---|---|---|---|
| Nyanja | Ujifunzaji wa Mashine | Ujifunzaji wa Mashine | Ujifunzaji wa Mashine |
| Familia | Machine learning | Machine learning | Machine learning |
| Mwaka wa asili≠ | 1997 | 1984 | 2001 |
| Mwanzilishi≠ | Freund, Y. & Schapire, R.E. | Breiman, Friedman, Olshen & Stone | Breiman, L. |
| Aina≠ | Ensemble (sequential boosting of weak learners) | Recursive partitioning (if-then rules) | Ensemble (bagging of decision trees) |
| Chanzo asilia≠ | Freund, Y. & Schapire, R.E. (1997). A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences, 55(1), 119–139. 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 ↗ |
| Majina mbadala≠ | AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Zinazohusiana≠ | 5 | 5 | 4 |
| Muhtasari≠ | AdaBoost (Adaptive Boosting) is the original boosting algorithm, introduced by Yoav Freund and Robert Schapire in 1997, that combines a sequence of simple weak learners by giving more weight to the observations they get wrong. The forerunner of gradient boosting, it is simple, interpretable, and a strong baseline for classification. | 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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