What is a distinguishing feature between bagging and boosting techniques?

Group of answer choices

Bagging uses an ensemble of decision trees, while boosting typically relies on a single decision tree.

Bagging combines multiple weak learners to create a strong learner, while boosting sequentially improves the performance of a single weak learner.

Bagging reduces variance by averaging predictions from multiple models, while boosting reduces bias by focusing on misclassified instances.

Bagging assigns weights to training instances based on their importance, while boosting equally weights all training instances.