পদ্ধতির তুলনা করুন

	ব্যাখ্যাযোগ্য আইসোলেশন ফরেস্ট	আইসোলেশন ফরেস্ট
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2008 / 2017	2008
প্রবর্তক	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.
ধরন	Anomaly detection with post-hoc explainability	Unsupervised ensemble (random partitioning trees)
মৌলিক উৎস	Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.
অপর নাম	XIF, Isolation Forest with SHAP, interpretable anomaly detection, explainable anomaly isolation	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
সম্পর্কিত	5	5
সারসংক্ষেপ	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.