Porovnať metódy
Prezrite si vybrané metódy vedľa seba; riadky, ktoré sa líšia, sú zvýraznené.
| CatBoost× | Rozhodovací strom× | Náhodný les× | |
|---|---|---|---|
| Odbor | Strojové učenie | Strojové učenie | Strojové učenie |
| Rodina | Machine learning | Machine learning | Machine learning |
| Rok vzniku≠ | 2018 | 1984 | 2001 |
| Tvorca≠ | Prokhorenkova, L. et al. (Yandex) | Breiman, Friedman, Olshen & Stone | Breiman, L. |
| Typ≠ | Gradient boosting on decision trees | Recursive partitioning (if-then rules) | Ensemble (bagging of decision trees) |
| Pôvodný zdroj≠ | 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 ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Ďalšie názvy≠ | CatBoost (Categorical Boosting), categorical boosting, ordered boosting, kategorik gradyan artırma | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Príbuzné≠ | 5 | 5 | 4 |
| Zhrnutie≠ | 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. | 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. |
| ScholarGateDátová sada ↗ |
|
|
|