Which ensemble method involves combining multiple models using weighted voting?Review LaterBaggingBoostingStackingVoting

The ensemble method that involves combining multiple models using weighted voting is Boosting.

Explanation:

1. Bagging (Bootstrap Aggregating) builds multiple models independently and combines their predictions through voting (or averaging) without focusing on errors from individual models.

2. Boosting focuses on combining weaker models (often decision trees) sequentially, with each new model being trained to correct the errors made by the previous ones. The final prediction is made through weighted voting, where models that perform better contribute more to the final prediction.

3. Stacking combines multiple models by training a meta-learner on their predictions to improve overall performance.

4. Voting can also involve combining models using simple voting, but it does not inherently involve weighting; it's typically seen as an equal contribution from all models.

Thus, boosting is the technique that explicitly involves weighted voting based on model performance.