Answer: (iii) It can be used in both classification as well as regression — corrected from typo second (ii)

Explanation — Averaging Ensemble:

• Bagging / Voting / Averaging: Train $M$ base learners on bootstrap samples, average predictions: $\hat{y}= \frac1M\sum_m f_m(x)$ for regression, or soft voting / probability averaging for classification.
• Classification: averaging predicted probabilities or logits, e.g., Random Forest classification by majority vote (which is averaging indicator) or averaging probabilities.
• Regression: direct average of continuous outputs, e.g., Random Forest regressor.
• Reduces variance, improves generalization.

So NOT limited to only classification or only regression. Hence (iii) Both (intended as third option despite mislabeled).

Options had typo: listed (ii) twice; correct mapping: (iii) It can be used in both...