Machine Learning MCQs with answers Page - 13

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📊 Machine Learning
Q. In PCA the number of input dimensiona are equal to principal components
  • (A) TRUE
  • (B) Statistics and Informal Retrieval
  • (C) ---
  • (D) ---
💬 Discuss
✅ Correct Answer: (A) TRUE
📊 Machine Learning
Q. What are the two methods used for the calibration in Supervised Learning?
  • (A) Platt Calibration and Isotonic Regression
  • (B) variable f1 is an example of ordinal variable
  • (C) Both A and B
  • (D) None of these
💬 Discuss
✅ Correct Answer: (A) Platt Calibration and Isotonic Regression
📊 Machine Learning
Q. A student Grade is a variable F1 which takes a value from A,B,C and D. Which of the following is True in the following case?
  • (A) variable f1 is an example of nominal variable
  • (B) nonlinear
  • (C) it doesnt belong to any of the mentioned categories
  • (D) it belongs to both ordinal and nominal category
💬 Discuss
✅ Correct Answer: (B) nonlinear
📊 Machine Learning
Q. Regression trees are often used to model . . . . . . . . data.
  • (A) linear
  • (B) proababilistic model
  • (C) categorical
  • (D) symmetrical
💬 Discuss
✅ Correct Answer: (B) proababilistic model
📊 Machine Learning
Q. Support Vector Machine is
  • (A) logical model
  • (B) We can not classify data correctly for given setting of hyper parameter C
  • (C) geometric model
  • (D) none of the above
💬 Discuss
✅ Correct Answer: (C) geometric model
📊 Machine Learning
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 it's 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) 2 and 3
  • (C) Can't Say
  • (D) None of these
💬 Discuss
✅ Correct Answer: (A) We can still classify data correctly for given setting of hyper parameter C
📊 Machine Learning
Q. Supervised learning and unsupervised clustering both require at least one
  • (A) hidden attribute.
  • (B) output attribute.
  • (C) input attribute.
  • (D) categorical attribute.
💬 Discuss
✅ Correct Answer: (A) hidden attribute.
📊 Machine Learning
Q. Which of the following sentences are correct in reference to Information gain? a. It is biased towards single-valued attributes b. It is biased towards multi-valued attributes c. ID3 makes use of information gain d. The approact used by ID3 is greedy
  • (A) a and b
  • (B) a and d
  • (C) b, c and d
  • (D) all of the above
💬 Discuss
✅ Correct Answer: (C) b, c and d
📊 Machine Learning
Q. Even if there are no actual supervisors . . . . . . . . learning is also based on feedback provided by the environment
  • (A) Supervised
  • (B) Reinforcement
  • (C) Unsupervised
  • (D) None of the above
💬 Discuss
✅ Correct Answer: (B) Reinforcement
📊 Machine Learning
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?
  • (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
💬 Discuss
✅ Correct Answer: (B) Feature F1 is an example of ordinal variable.

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