Machine Learning Set 31

Computer Science Engineering (CSE)

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This set of Machine Learning (ML) Multiple Choice Questions & Answers (MCQs) focuses on Machine Learning Set 31

Q1 | Suppose, you got a situation where you find that your linear regression model is under fitting the data. In such situation which of the following options would you consider?1. I will add more variables2. I will start introducing polynomial degree variables3. I will remove some variables
  • 1 and 2
  • 2 and 3
  •  1 and 3
  • 1, 2 and 3
Answer: 1 and 2
Q2 | The minimum time complexity for training an SVM is O(n2). According to this fact, what sizes of datasets are not best suited for SVM’s?
  • Large datasets
  • Small datasets
  • Medium sized datasets
  • Size does not matter
Answer: Large datasets
Q3 | We usually use feature normalization before using the Gaussian kernel in SVM. What is true about feature normalization? 1. We do feature normalization so that new feature will dominate other 2. Some times, feature normalization is not feasible in case of categorical variables3. Feature normalization always helps when we use Gaussian kernel in SVM
  • 1
  • 1 and 2
  • 1 and 3
  • 2 and 3
Answer: 1 and 2
Q4 | Suppose you are using RBF kernel in SVM with high Gamma value. What does this signify?
  • The model would consider even far away points from hyperplane for modeling
  • The model would consider only the points close to the hyperplane for modeling
  • The model would not be affected by distance of points from hyperplane for modeling
  • None of the above
Answer: The model would consider only the points close to the hyperplane for modeling
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