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| Bajezijanski XGBoost× | Градијентно појачање× | Slučajna šuma× | |
|---|---|---|---|
| Oblast | Mašinsko učenje | Mašinsko učenje | Mašinsko učenje |
| Porodica | Machine learning | Machine learning | Machine learning |
| Godina nastanka≠ | 2012–2016 | 2001 | 2001 |
| Tvorac≠ | Chen, T. & Guestrin, C. (XGBoost); Snoek, J. et al. (Bayesian Optimization) | Friedman, J. H. | Breiman, L. |
| Tip≠ | Ensemble (gradient boosted trees with Bayesian hyperparameter search) | Ensemble (sequential boosting of decision trees) | Ensemble (bagging of decision trees) |
| Temeljni izvor≠ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. DOI ↗ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Drugi nazivi | Bayesian XGBoost, XGBoost with Bayesian Optimization, BayesOpt-XGBoost, Bayes-tuned XGBoost | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Srodne≠ | 4 | 5 | 4 |
| Sažetak≠ | Bayesian XGBoost combines the predictive power of Extreme Gradient Boosting with Bayesian optimization for hyperparameter tuning. Instead of grid or random search, a probabilistic surrogate model guides the search for optimal learning rate, tree depth, and regularization parameters, achieving near-peak performance with far fewer evaluations than exhaustive search approaches. | 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. | 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. |
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