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Ensemble Methods & Technique Interview Questions & Answers in 2024

  • September 09, 2022
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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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