how ensemble techniques works?

Question

Accepted Answer

Ensemble techniques combine multiple models to improve prediction accuracy.

Ensemble techniques can be used with various types of models, such as decision trees, neural networks, and support vector machines.
Common ensemble techniques include bagging, boosting, and stacking.
Bagging involves training multiple models on different subsets of the data and combining their predictions through averaging or voting.
Boosting involves iteratively training models on the data, with each subsequent model focusing on the samples that were misclassified by the previous models.
Stacking involves training multiple models and using their predictions as input features for a final model.
Ensemble techniques can help reduce overfitting and improve generalization performance.
Ensemble techniques have been successfully applied in various domains, such as finance, healthcare, and image recognition.

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