Comparar métodos

	LightGBM	Isolation Forest	Random Forest
Campo	Aprendizaje automático	Aprendizaje automático	Aprendizaje automático
Familia	Machine learning	Machine learning	Machine learning
Año de origen	2017	2008	2001
Autor original	Ke, G. et al. (Microsoft)	Liu, F.T., Ting, K.M. & Zhou, Z.-H.	Breiman, L.
Tipo	Gradient boosting decision tree ensemble	Unsupervised ensemble (random partitioning trees)	Ensemble (bagging of decision trees)
Fuente seminal	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.	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	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Relacionados	5	5	4
Resumen	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.	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.