Сравнение на методи

	LightGBM	Дърво на решенията	Isolation Forest
Област	Машинно обучение	Машинно обучение	Машинно обучение
Семейство	Machine learning	Machine learning	Machine learning
Година на възникване	2017	1984	2008
Създател	Ke, G. et al. (Microsoft)	Breiman, Friedman, Olshen & Stone	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
Тип	Gradient boosting decision tree ensemble	Recursive partitioning (if-then rules)	Unsupervised ensemble (random partitioning trees)
Основополагащ източник	Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q. & Liu, T.-Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems (NeurIPS) 30, 3146–3154.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.
Други названия	LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
Свързани	5	5	5
Резюме	LightGBM is Microsoft's gradient boosting decision tree implementation, introduced by Ke and colleagues in 2017, that grows trees leaf-wise and bins features into histograms for speed. On large datasets it is much faster than XGBoost while retaining strong predictive accuracy.	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.	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.