![]() The combined estimator is usually better than any of the single baseĮstimator because its variance is reduced.Įxamples: Bagging methods, Forests of randomized trees, …īy contrast, in boosting methods, base estimators are built sequentiallyĪnd one tries to reduce the bias of the combined estimator. In averaging methods, the driving principle is to build severalĮstimators independently and then to average their predictions. Two families of ensemble methods are usually distinguished: ![]() Generalizability / robustness over a single estimator. The goal of ensemble methods is to combine the predictions of severalīase estimators built with a given learning algorithm in order to improve Using the VotingClassifier with GridSearchCV Weighted Average Probabilities (Soft Voting) Majority Class Labels (Majority/Hard Voting)
0 Comments
Leave a Reply. |