Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Agregación por Bootstrap (Bagging)× | Gradient Boosting× | Isolation Forest× | |
|---|---|---|---|
| Campo | Aprendizaje automático | Aprendizaje automático | Aprendizaje automático |
| Familia | Machine learning | Machine learning | Machine learning |
| Año de origen≠ | 1996 | 2001 | 2008 |
| Autor original≠ | Breiman, L. | Friedman, J. H. | Liu, F.T., Ting, K.M. & Zhou, Z.-H. |
| Tipo≠ | Ensemble meta-algorithm (variance reduction via bootstrap aggregation) | Ensemble (sequential boosting of decision trees) | Unsupervised ensemble (random partitioning trees) |
| Fuente seminal≠ | Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140. DOI ↗ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ |
| Alias≠ | Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection |
| Relacionados | 5 | 5 | 5 |
| Resumen≠ | Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner. | Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost. | 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. |
| ScholarGateConjunto de datos ↗ |
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