Sammenlign metoder
Gennemgå dine valgte metoder side om side; rækker, der afviger, er fremhævet.
| CatBoost× | Gradient Boosting× | Random Forest× | |
|---|---|---|---|
| Fagområde | Maskinlæring | Maskinlæring | Maskinlæring |
| Familie | Machine learning | Machine learning | Machine learning |
| Oprindelsesår≠ | 2018 | 2001 | 2001 |
| Ophavsperson≠ | Prokhorenkova, L. et al. (Yandex) | Friedman, J. H. | Breiman, L. |
| Type≠ | Gradient boosting on decision trees | Ensemble (sequential boosting of decision trees) | Ensemble (bagging of decision trees) |
| Oprindelig kilde≠ | Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V. & Gulin, A. (2018). CatBoost: Unbiased Boosting with Categorical Features. In NeurIPS 2018. 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 ↗ |
| Aliasser | CatBoost (Categorical Boosting), categorical boosting, ordered boosting, kategorik gradyan artırma | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Relaterede≠ | 5 | 5 | 4 |
| Resumé≠ | CatBoost is a gradient boosting algorithm, introduced by Prokhorenkova and colleagues at Yandex in 2018, that handles categorical variables natively and uses ordered target encoding to avoid label leakage. By building an additive ensemble of trees while permuting the data order at each iteration, it is often superior to XGBoost and LightGBM on category-heavy data. | 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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