Comparer des méthodes

	LightGBM	Isolation Forest	Régression logistique	Forêt Aléatoire
Domaine	Apprentissage automatique	Apprentissage automatique	Statistiques de recherche	Apprentissage automatique
Famille	Machine learning	Machine learning	Process / pipeline	Machine learning
Année d'origine	2017	2008	1958	2001
Auteur d'origine	Ke, G. et al. (Microsoft)	Liu, F.T., Ting, K.M. & Zhou, Z.-H.	David Roxbee Cox	Breiman, L.
Type	Gradient boosting decision tree ensemble	Unsupervised ensemble (random partitioning trees)	Method	Ensemble (bagging of decision trees)
Source fondatrice	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.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Alias	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	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Apparentées	5	5	3	4
Résumé	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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.