Machine Learning MCQs with answers Page - 5

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Q. The average squared difference between classifier predicted output and actual output.

  • (A) mean squared error
  • (B) root mean squared error
  • (C) mean absolute error
  • (D) mean relative error

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Q. Which of the following methods do we use to find the best fit line for data in Linear Regression?

  • (A) Least Square Error
  • (B) Maximum Likelihood
  • (C) Logarithmic Loss
  • (D) Both A and B

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Q. Following are the descriptive models

  • (A) clustering
  • (B) classification
  • (C) association rule
  • (D) both a and c

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Q. Assume that you are given a data set and a neural network model trained on the data set. You are asked to build a decision tree model with the sole purpose of understanding/interpreting the built neural network model. In such a scenario, which among the following measures would you concentrate most on optimising?

  • (A) accuracy of the decision tree model on the given data set
  • (B) f1 measure of the decision tree model on the given data set
  • (C) fidelity of the decision tree model, which is the fraction of instances on which the neural network and the decision tree give the same output
  • (D) comprehensibility of the decision tree model, measured in terms of the size of the corresponding rule set

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Q. What are common feature selection methods in regression task?

  • (A) correlation coefficient
  • (B) greedy algorithms
  • (C) all above
  • (D) none of these

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Q. Which of the following can only be used when training data are linearlyseparable?

  • (A) linear hard-margin svm
  • (B) linear logistic regression
  • (C) linear soft margin svm
  • (D) the centroid method

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Q. Wrapper methods are hyper-parameter selection methods that

  • (A) should be used whenever possible because they are computationally efficient
  • (B) should be avoided unless there are no other options because they are always prone to overfitting.
  • (C) are useful mainly when the learning machines are "black boxes"
  • (D) should be avoided altogether.

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Q. Given that we can select the same feature multiple times during the recursive partitioning of the input space, is it always possible to achieve 100% accuracy on the training data (given that we allow for trees to grow to their maximum size) when building decision trees?

  • (A) Yes
  • (B) 2
  • (C) ---
  • (D) ---

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Q. What characterize unlabeled examples in machine learning

  • (A) there is no prior knowledge
  • (B) there is no confusing knowledge
  • (C) there is prior knowledge
  • (D) there is plenty of confusing knowledge

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Q. What characterize is hyperplance in geometrical model of machine learning?

  • (A) a plane with 1 dimensional fewer than number of input attributes
  • (B) a plane with 2 dimensional fewer than number of input attributes
  • (C) a plane with 1 dimensional more than number of input attributes
  • (D) a plane with 2 dimensional more than number of input attributes

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