Comparar métodos

	Árbol de Decisión	Isolation Forest	Random Forest
Campo	Aprendizaje automático	Aprendizaje automático	Aprendizaje automático
Familia	Machine learning	Machine learning	Machine learning
Año de origen	1984	2008	2001
Autor original	Breiman, Friedman, Olshen & Stone	Liu, F.T., Ting, K.M. & Zhou, Z.-H.	Breiman, L.
Tipo	Recursive partitioning (if-then rules)	Unsupervised ensemble (random partitioning trees)	Ensemble (bagging of decision trees)
Fuente seminal	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.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Alias	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	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	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.	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.