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Dear candidates you will find MCQ questions of Machine Learning (ML) here. Learn these questions and prepare yourself for coming examinations and interviews. You can check the right answer of any question by clicking on any option or by clicking view answer button.

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Mr. Dubey • 51.43K Points
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Q. 261) Which of the following is true about bagging?
1. Bagging can be parallel
2. The aim of bagging is to reduce bias not variance
3. Bagging helps in reducing overfitting

(A) 1 and 2
(B) 2 and 3
(C) 1 and 3
(D) all of these
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Mr. Dubey • 51.43K Points
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Q. 262) 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

(A) you will have only k features after the first stage
(B) you will have only m features after the first stage
(C) you will have k+m features after the first stage
(D) you will have k*n features after the first stage
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Mr. Dubey • 51.43K Points
Coach

Q. 263) Which of the following is the difference between stacking and blending?

(A) stacking has less stable cv compared to blending
(B) in blending, you create out of fold prediction
(C) stacking is simpler than blending
(D) none of these
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Mr. Dubey • 51.43K Points
Coach

Q. 264) 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

(A) 1 and 2
(B) 2 and 3
(C) 1 and 3
(D) all of above
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Mr. Dubey • 51.43K Points
Coach

Q. 265) Q25. Which of the following are advantages of stacking?
1) More robust model
2) better prediction
3) Lower time of execution

(A) 1 and 2
(B) 2 and 3
(C) 1 and 3
(D) all of the above
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Mr. Dubey • 51.43K Points
Coach

Q. 266) 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

(A) 1 and 2
(B) 2 and 3
(C) 1 and 3
(D) all of above
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Mr. Dubey • 51.43K Points
Coach

Q. 267) 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

(A) 1 and 3
(B) 2 and 3
(C) 1 and 2
(D) 1, 2 and 3
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Mr. Dubey • 51.43K Points
Coach

Q. 268) Which of the following is true about averaging ensemble?

(A) it can only be used in classification problem
(B) it can only be used in regression problem
(C) it can be used in both classification as well as regression
(D) none of these
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Mr. Dubey • 51.43K Points
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Q. 269) 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

(A) 1 and 2
(B) 1 and 3
(C) 2 and 3
(D) all of above
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Mr. Dubey • 51.43K Points
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Q. 270) 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?

(A) feature f1 is an example of nominal variable.
(B) feature f1 is an example of ordinal variable.
(C) it doesn't belong to any of the above category.
(D) both of these
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