পদ্ধতির তুলনা করুন

	ব্যাখ্যাযোগ্য আইসোলেশন ফরেস্ট	ব্যাখ্যাযোগ্য র‍্যান্ডম ফরেস্ট
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2008 / 2017	2001–2017
প্রবর্তক	Liu, F. T., Ting, K. M., & Zhou, Z.-H. (Isolation Forest); Lundberg, S. M. & Lee, S.-I. (SHAP explainability layer)	Breiman, L. (RF); Lundberg & Lee (SHAP attribution)
ধরন	Anomaly detection with post-hoc explainability	Interpretable ensemble (bagging + post-hoc attribution)
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.	Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.
অপর নাম	XIF, Isolation Forest with SHAP, interpretable anomaly detection, explainable anomaly isolation	XRF, interpretable random forest, transparent random forest, random forest with explainability
সম্পর্কিত	5	4
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.	Explainable Random Forest (XRF) combines the predictive power of Breiman's Random Forest ensemble with systematic post-hoc attribution methods — principally SHAP values and mean-decrease-in-impurity importance — to make model decisions transparent and auditable. It delivers both high accuracy and human-interpretable feature contributions, satisfying demands from regulators, domain experts, and academic reviewers alike.