Machine Learning: MCQs Set – 20

Q191: Factors which affect performance of a learner system does not include

  • (A) Representation scheme used
  • (B) Training scenario
  • (C) Type of feedback
  • (D) Good data structures

Q192: For bi-variate data exploration, _____ is an effective tool.

  • (A) Box plot
  • (B) Two-way cross-tab
  • (C) Histogram
  • (D) None of the above

Q193: Ordinal data can be naturally ____.

  • (A) Measured
  • (B) Ordered
  • (C) Divided
  • (D) None of the above

Q194: Out of 200 emails, a classification model correctly predicted 150 spam emails and 30 ham emails. What is the error rate of the model?

  • (A) 10%
  • (B) 90%
  • (C) 80%
  • (D) none of the above

Q195: Hamming distance between binary vectors 1001 and 0101 is

  • (A) 1
  • (B) 2
  • (C) 3
  • (D) 4

Q196: The reason the Bayesian interpretation can be used to model the uncertainty of events is that it does not expect the long run frequencies of the events to happen.

  • (A) True
  • (B) False

Q187: One main disadvantage of Bayesian classifiers is that they utilize all available parameters to subtly change the predictions.

  • (A) True
  • (B) False

Q198: Classification is a type of supervised learning where a target feature, which is of categorical type, is predicted for the test data on the basis of the information imparted by the training data. The target categorical feature is known as?

  • (A) Object
  • (B) Variable
  • (C) Method
  • (D) Class

Q199: ———- is a line that linearly separates and classifies a set of data.

  • (A) Hyperplane
  • (B) Soft Margin
  • (C) Linear Margin
  • (D) Support Vectors

Q200: Which of the following options is true about the kNN algorithm?

  • (A) It can be used only for classification
  • (B) It can be used only for regression
  • (C) It can be used for both classification and regression
  • (D) It is not possible to use for both classification and regression



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