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Ensemble Methods & Technique Interview Questions & Answers in 2024

  • September 09, 2022
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Bharani Kumar Depuru is a well known IT personality from Hyderabad. He is the Founder and Director of Innodatatics Pvt Ltd and 360DigiTMG. Bharani Kumar is an IIT and ISB alumni with more than 17 years of experience, he held prominent positions in the IT elites like HSBC, ITC Infotech, Infosys, and Deloitte. He is a prevalent IT consultant specializing in Industrial Revolution 4.0 implementation, Data Analytics practice setup, Artificial Intelligence, Big Data Analytics, Industrial IoT, Business Intelligence and Business Management. Bharani Kumar is also the chief trainer at 360DigiTMG with more than Ten years of experience and has been making the IT transition journey easy for his students. 360DigiTMG is at the forefront of delivering quality education, thereby bridging the gap between academia and industry.

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  • 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 –

    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)

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