পদ্ধতির তুলনা করুন

	আধা-পর্যবেক্ষিত আইসোলেশন ফরেস্ট	আইসোলেশন ফরেস্ট
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2013–2020	2008
প্রবর্তক	Extended from Liu, F.T., Ting, K.M., and Zhou, Z-H. (iForest, 2008); semi-supervised variants developed by multiple authors ca. 2013–2020	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
ধরন	Ensemble anomaly detection (semi-supervised extension)	Unsupervised ensemble (random partitioning trees)
মৌলিক উৎস	Görnitz, N., Kloft, M., Rieck, K., & Brefeld, U. (2013). Toward supervised anomaly detection. Journal of Artificial Intelligence Research, 46, 235–262.	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.
অপর নাম	SSIF, semi-supervised iForest, label-guided Isolation Forest, partially supervised Isolation Forest	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
সম্পর্কিত	6	5
সারসংক্ষেপ	Semi-supervised Isolation Forest extends the classic Isolation Forest anomaly detector by incorporating a small set of labeled anomaly (and possibly normal) examples alongside a large unlabeled dataset. This label guidance adjusts the model's anomaly scores so that known anomalies are separated more reliably, bridging the gap between fully unsupervised and fully supervised detection.	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.