Don't rely on one opinion combine many opinions. So instead of trusting one tree, we: build many trees and combine their answers

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Don't rely on one opinion combine many opinions. So instead of trusting one tree, we: build many trees and combine their answers

Ensemble learning is the idea of using multiple models and combining their predictions rather than relying on a single model. By building many trees and blending their outputs, you’re aiming to get a more robust and accurate overall prediction, since different models can compensate for each other’s errors and reduce variability. This description captures the general principle of ensemble methods—the collective decision of several models—without specifying a particular technique. Bagging, boosting, and stacking are all specific ways to implement ensembles, but the statement describes the broader concept of using multiple trees and aggregating their answers, which is why the umbrella term Ensemble Techniques is the best fit.

Ensemble learning is the idea of using multiple models and combining their predictions rather than relying on a single model. By building many trees and blending their outputs, you’re aiming to get a more robust and accurate overall prediction, since different models can compensate for each other’s errors and reduce variability.

This description captures the general principle of ensemble methods—the collective decision of several models—without specifying a particular technique. Bagging, boosting, and stacking are all specific ways to implement ensembles, but the statement describes the broader concept of using multiple trees and aggregating their answers, which is why the umbrella term Ensemble Techniques is the best fit.

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