Machine Learning (ML) MCQs | Page - 33

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.

M

Mr. Dubey • 97.30K Points
Coach

Q. If you remove the non-red circled points from the data, the decision boundary will change?

  • (A) true
  • (B) false
  • (C) ---
  • (D) ---
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M

Mr. Dubey • 97.30K Points
Coach

Q. Suppose you are building a SVM model on data X. The data X can be error prone which means that you should not trust any specific data point too much. Now think that you want to build a SVM model which has quadratic kernel function of polynomial degree 2 that uses Slack variable C as one of its hyper parameter.What would happen when you use very large value of C(C->infinity)?

  • (A) we can still classify data correctly for given setting of hyper parameter c
  • (B) we can not classify data correctly for given setting of hyper parameter c
  • (C) can�t say
  • (D) none of these
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M

Mr. Dubey • 97.30K Points
Coach

Q. The objective of the support vector machine algorithm is to find a hyperplane in an N-dimensional space(N the number of features) that distinctly classifies the data points.

  • (A) true
  • (B) false
  • (C) ---
  • (D) ---
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M

Mr. Dubey • 97.30K Points
Coach

Q. Hyperplanes are decision boundaries that help classify the data points.

  • (A) true
  • (B) false
  • (C) ---
  • (D) ---
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M

Mr. Dubey • 97.30K Points
Coach

Q. SVMalgorithmsusea set of mathematical functions that are defined as thekernel.

  • (A) true
  • (B) false
  • (C) ---
  • (D) ---
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M

Mr. Dubey • 97.30K Points
Coach

Q. In SVM, Kernel function is used to map a lower dimensional data into a higher dimensional data.

  • (A) true
  • (B) false
  • (C) ---
  • (D) ---
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M

Mr. Dubey • 97.30K Points
Coach

Q. Which of the following is true about Naive Bayes ?

  • (A) assumes that all the features in a dataset are equally important
  • (B) assumes that all the features in a dataset are independent
  • (C) both a and b
  • (D) none of the above option
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M

Mr. Dubey • 97.30K Points
Coach

Q. Which of the following isnotsupervised learning?

  • (A) ��pca
  • (B) ��decision tree
  • (C) ��naive bayesian
  • (D) linerar regression
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