Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Isolation Forest× | Forêt Aléatoire× | |
|---|---|---|
| Domaine | Apprentissage automatique | Apprentissage automatique |
| Famille | Machine learning | Machine learning |
| Année d'origine≠ | 2008 | 2001 |
| Auteur d'origine≠ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. | Breiman, L. |
| Type≠ | Unsupervised ensemble (random partitioning trees) | Ensemble (bagging of decision trees) |
| Source fondatrice≠ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Alias≠ | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Apparentées≠ | 5 | 4 |
| Résumé≠ | Isolation Forest is an unsupervised machine-learning method for anomaly and outlier detection, introduced by Liu, Ting and Zhou in 2008, that isolates anomalies through random partitioning of the data. It works without any labelled anomaly data and scales to high-dimensional datasets. | 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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