Machine Learning (ML) MCQs | Page - 27
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Q. Suppose you are using stacking with n different machine learning algorithms with k folds on data.
Which of the following is true about one level (m base models + 1 stacker) stacking?
Note:
Here, we are working on binary classification problem
All base models are trained on all features
You are using k folds for base models
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Q. Which of the following is the difference between stacking and blending?
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Q. Which of the following can be one of the steps in stacking?
1. Divide the training data into k folds
2. Train k models on each k-1 folds and get the out of fold predictions for remaining one fold
3. Divide the test data set in “k” folds and get individual fold predictions by different algorithms
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Q. Q25. Which of the following are advantages of stacking?
1) More robust model
2) better prediction
3) Lower time of execution
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Q. Which of the following are correct statement(s) about stacking?
A machine learning model is trained on predictions of multiple machine learning models
A Logistic regression will definitely work better in the second stage as compared to other classification methods
First stage models are trained on full / partial feature space of training data
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Q. Which of the following is true about weighted majority votes?
1. We want to give higher weights to better performing models
2. Inferior models can overrule the best model if collective weighted votes for inferior models is higher than best model
3. Voting is special case of weighted voting
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Q. Which of the following is true about averaging ensemble?
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Q. How can we assign the weights to output of different models in an ensemble?
1. Use an algorithm to return the optimal weights
2. Choose the weights using cross validation
3. Give high weights to more accurate models
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Q. A feature F1 can take certain value: A, B, C, D, E, & F and represents grade of students from a college. Which of the following statement is true in following case?
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Q. What would you do in PCA to get the same projection as SVD?
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