Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Machine à vecteurs de support (Classification)× | Forêt Aléatoire× | |
|---|---|---|
| Domaine | Apprentissage automatique | Apprentissage automatique |
| Famille | Machine learning | Machine learning |
| Année d'origine≠ | 1995 | 2001 |
| Auteur d'origine≠ | Cortes, C. & Vapnik, V. | Breiman, L. |
| Type≠ | Maximum-margin classifier (kernel method) | Ensemble (bagging of decision trees) |
| Source fondatrice≠ | Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297. DOI ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Alias | Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Apparentées≠ | 5 | 4 |
| Résumé≠ | The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data. | 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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