Ensemble Methods in Machine Learning
GPTKB entity
Statements (33)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
Machine Learning Technique
|
| gptkbp:appliesTo |
Regression
Classification |
| gptkbp:canBe |
gptkb:Neural_Networks
gptkb:Support_Vector_Machines Heterogeneous Homogeneous Decision Trees |
| gptkbp:combines |
Multiple models
|
| gptkbp:example |
gptkb:CatBoost
gptkb:LightGBM gptkb:XGBoost gptkb:Gradient_Boosting gptkb:AdaBoost |
| https://www.w3.org/2000/01/rdf-schema#label |
Ensemble Methods in Machine Learning
|
| gptkbp:improves |
Generalization
Computational cost Model robustness |
| gptkbp:includes |
gptkb:Bagging
gptkb:Random_Forest gptkb:Voting Stacking Boosting |
| gptkbp:introducedIn |
1990s
|
| gptkbp:popularizedBy |
gptkb:Leo_Breiman
|
| gptkbp:reduces |
Variance
Overfitting Bias |
| gptkbp:relatedTo |
Bias-variance tradeoff
|
| gptkbp:requires |
Diversity among models
|
| gptkbp:usedFor |
Improving predictive performance
|
| gptkbp:bfsParent |
gptkb:Gina_Kuncheva
|
| gptkbp:bfsLayer |
7
|