Ensemble Methods & Technique Interview Questions & Answers in 2025

Ensemble learning refers to ______________.

• a) Combining the predictions from two or more models.
• b) Only visualizing the predictions from models.
• c) Removing variables from model.
• d) None of the above.

Answer - b) Only visualizing the predictions from models

Ensembles for classification are best understood by the _______________.

• a) Combination of hyper planes of members.
• b) Combination of decision boundaries of members.
• c) Both (a) and (b).
• d) None of the above.

Answer - b) Combination of decision boundaries of members

Ensembles for regression are best understood by the ____________.

• a) Combination of hyperplanes of members.
• b) Combination of decision boundaries of members.
• c) Both (a) and (b).
• d) None of the above.

Answer - a) Combination of hyperplanes of members

Ensemble methods is/are ______________.

• a) Bagging.
• b) Boosting.
• c) Stacking.
• d) All of the above.

Answer - d) All the above

Stacking builds ensembles in ____________.

• a) Series.
• b) Parallel.
• c) Series and parallel.
• d) None of the above.

Answer - b) Parallel

Boosting builds ensembles in ___________.

• a) Series.
• b) Parallel.
• c) Series and parallel.
• d) None of the above.

Answer - a) Series

Ensemble methods seek to ___________.

• a) Reduce variance of individual weak learners by aggregating their predictions.
• b) Improve performance by exploiting prediction diversity.
• c) Both (a) and (b).
• d) None of the above.

Answer - c) Both (a) and (b)

___________ uses ensembles to reduce the variability of single ML models.

• a) Bagging.
• b) Boosting.
• c) Stacking/Blending.
• d) None of the above.

Answer - a) Bagging

____________ uses ensembles to capture different characteristics of a task, learning how to combine them.

• a) Bagging.
• b) Boosting.
• c) Stacking.
• d) None of the above.

Answer - c) Stacking

___________ uses ensembles of ML models each capturing a specific subspace of predictor space.

• a) Bagging.
• b) Boosting.
• c) Stacking.
• d) None of the above.

Answer - b) Boosting

Training in parallel that occurs in bagging aims to capitalize on the __________ of each base learner, while the sequential training in boosting capitalizes on the ________ of the learners.

• a) Independence , dependence.
• b) Dependence, Independence.
• c) Dependence , Dependence.
• d) Independence, Independence.

Answer - a) Independence , dependence

Bagging aims to –

• a) Decrease variance, not bias.
• b) Decrease bias, not variance.
• c) Increase bias, not variance.
• d) Increase variance, not bias.

Answer - a) Decrease variance, not bias

Boosting aims to –

• a) Decrease variance, not bias.
• b) Decrease bias, not variance.
• c) Increase bias, not variance.
• d) Increase variance, not bias.

Answer - b) Decrease bias, not variance

Which of the below are ensemble algebraic combinational rule –

• a) Mean rule.
• b) Sum rule.
• c) Weighted sum rule.
• d) All of the above.

Answer - d) All of the above

Which of the bensemble algebraic combinational rule elow are ensemble voting based combinational rule –

• a) Majority (plurality) voting.
• b) Weighted majority voting.
• c) Both (a) and (b).
• d) None of the above.

Answer - c) Both (a) and (b)