Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Объяснимый Isolation Forest× | Isolation Forest× | |
|---|---|---|
| Область | Машинное обучение | Машинное обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 2008 / 2017 | 2008 |
| Автор метода≠ | Liu, F. T., Ting, K. M., & Zhou, Z.-H. (Isolation Forest); Lundberg, S. M. & Lee, S.-I. (SHAP explainability layer) | Liu, F.T., Ting, K.M. & Zhou, Z.-H. |
| Тип≠ | Anomaly detection with post-hoc explainability | Unsupervised ensemble (random partitioning trees) |
| Основополагающий источник≠ | Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774. link ↗ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ |
| Другие названия≠ | XIF, Isolation Forest with SHAP, interpretable anomaly detection, explainable anomaly isolation | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection |
| Связанные | 5 | 5 |
| Сводка≠ | Explainable Isolation Forest combines the Isolation Forest anomaly detection algorithm with post-hoc explainability tools — most commonly SHAP (SHapley Additive exPlanations) — to not only flag anomalous observations but also reveal which features drove each anomaly score. It bridges unsupervised anomaly detection with the interpretability demands of regulated and high-stakes domains. | 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. |
| ScholarGateНабор данных ↗ |
|
|