Methoden vergleichen

	LightGBM	Isolation Forest	Logistische Regression
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen	Forschungsstatistik
Familie	Machine learning	Machine learning	Process / pipeline
Entstehungsjahr	2017	2008	1958
Urheber	Ke, G. et al. (Microsoft)	Liu, F.T., Ting, K.M. & Zhou, Z.-H.	David Roxbee Cox
Typ	Gradient boosting decision tree ensemble	Unsupervised ensemble (random partitioning trees)	Method
Wegweisende Quelle	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.	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Aliasnamen	LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection	logit model, binomial logistic regression, LR
Verwandt	5	5	3
Zusammenfassung	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.	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.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.