Sammenlign metoder
Gennemgå dine valgte metoder side om side; rækker, der afviger, er fremhævet.
| AdaBoost× | CatBoost× | LightGBM× | |
|---|---|---|---|
| Fagområde | Maskinlæring | Maskinlæring | Maskinlæring |
| Familie | Machine learning | Machine learning | Machine learning |
| Oprindelsesår≠ | 1997 | 2018 | 2017 |
| Ophavsperson≠ | Freund, Y. & Schapire, R.E. | Prokhorenkova, L. et al. (Yandex) | Ke, G. et al. (Microsoft) |
| Type≠ | Ensemble (sequential boosting of weak learners) | Gradient boosting on decision trees | Gradient boosting decision tree ensemble |
| Oprindelig kilde≠ | Freund, Y. & Schapire, R.E. (1997). A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences, 55(1), 119–139. DOI ↗ | Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V. & Gulin, A. (2018). CatBoost: Unbiased Boosting with Categorical Features. In NeurIPS 2018. DOI ↗ | 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. link ↗ |
| Aliasser≠ | AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma | CatBoost (Categorical Boosting), categorical boosting, ordered boosting, kategorik gradyan artırma | LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting |
| Relaterede | 5 | 5 | 5 |
| Resumé≠ | AdaBoost (Adaptive Boosting) is the original boosting algorithm, introduced by Yoav Freund and Robert Schapire in 1997, that combines a sequence of simple weak learners by giving more weight to the observations they get wrong. The forerunner of gradient boosting, it is simple, interpretable, and a strong baseline for classification. | 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. | 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. |
| ScholarGateDatasæt ↗ |
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