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Ensemble Methods & Technique Interview Questions & Answers in 2024
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Table of Content
- Ensemble learning refers to ________
- Ensembles for classification are best understood by the ___________.
- Ensembles for regression are best understood by the ___________.
- Ensemble methods is/are _____________.
- Stacking builds ensembles in _____________.
- Boosting builds ensembles in _____________.
- Ensemble methods seek to ____________.
- __________ uses ensembles to reduce the variability of single ML models.
- _____________uses ensembles to capture different characteristics of a task, learning how to combine them.
- _____________uses ensembles of ML models each capturing a specific subspace of predictor space.
- 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.
- Bagging aims to –
- Boosting aims to –
- Which of the below are ensemble algebraic combinational rule –
- Which of the bensemble algebraic combinational rule elow are ensemble voting based combinational rule –
-
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 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)
