Machine Learning MCQs with answers Page - 11

Here, you will find a collection of MCQ questions on Machine Learning. Go through these questions to enhance your preparation for upcoming examinations and interviews.

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Q. Which of the following assumptions do we make while deriving linear regression parameters?
1. The true relationship between dependent y and predictor x is linear
2. The model errors are statistically independent
3. The errors are normally distributed with a 0 mean and constant standard deviation
4. The predictor x is non-stochastic and is measured error-free

  • (A) 1, 2 and 3
  • (B) 1,3 and 4
  • (C) 1 and 3
  • (D) All of above

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Q. For the given weather data, Calculate probability of not playing

  • (A) 0.4
  • (B) 0.64
  • (C) 0.36
  • (D) 0.5

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Q. In PCA the number of input dimensiona are equal to principal components

  • (A) TRUE
  • (B) Statistics and Informal Retrieval
  • (C) ---
  • (D) ---

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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

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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

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Q. Regression trees are often used to model . . . . . . . . data.

  • (A) linear
  • (B) proababilistic model
  • (C) categorical
  • (D) symmetrical

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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

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Q. Supervised learning and unsupervised clustering both require at least one

  • (A) hidden attribute.
  • (B) output attribute.
  • (C) input attribute.
  • (D) categorical attribute.

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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

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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

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