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| CatBoost× | Дрво одлучивања× | LightGBM× | |
|---|---|---|---|
| Oblast | Mašinsko učenje | Mašinsko učenje | Mašinsko učenje |
| Porodica | Machine learning | Machine learning | Machine learning |
| Godina nastanka≠ | 2018 | 1984 | 2017 |
| Tvorac≠ | Prokhorenkova, L. et al. (Yandex) | Breiman, Friedman, Olshen & Stone | Ke, G. et al. (Microsoft) |
| Tip≠ | Gradient boosting on decision trees | Recursive partitioning (if-then rules) | Gradient boosting decision tree ensemble |
| Temeljni izvor≠ | Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V. & Gulin, A. (2018). CatBoost: Unbiased Boosting with Categorical Features. In NeurIPS 2018. DOI ↗ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. 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 ↗ |
| Drugi nazivi≠ | CatBoost (Categorical Boosting), categorical boosting, ordered boosting, kategorik gradyan artırma | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree | LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting |
| Srodne | 5 | 5 | 5 |
| Sažetak≠ | 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. | 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. | 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. |
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