Machine Learning MCQ Questions and Answers Quiz

11. Adding more basis functions in a linear model... (pick the most probably option)

  1. Decreases model bias
  2. Decreases estimation bias
  3. Decreases variance
  4. Doesnt affect bias and variance

12. Another name for an output attribute.

  1. predictive variable
  2. independent variable
  3. estimated variable
  4. dependent variable

13. Bootstrapping allows us to

  1. choose the same training instance several times.
  2. choose the same test set instance several times.
  3. build models with alternative subsets of the training data several times.
  4. test a model with alternative subsets of the test data several times.

14. Choose the options that are correct regarding machine learning (ML) and artificial intelligence (AI),(A) ML is an alternate way of programming intelligent machines.(B) ML and AI have very different goals.(C) ML is a set of techniques that turns a dataset into a software.(D) AI is a software that can emulate the human mind.

  1. (A), (B), (D)
  2. (A), (C), (D)
  3. (B), (C), (D)
  4. All are correct

15. Classification problems are distinguished from estimation problems in that

  1. classification problems require the output attribute to be numeric.
  2. classification problems require the output attribute to be categorical.
  3. classification problems do not allow an output attribute.
  4. classification problems are designed to predict future outcome.

16. Computational complexity of Gradient descent is

  1. linear in D
  2. linear in N
  3. polynomial in D
  4. dependent on the number of iterations

17. Computers are best at learning

  1. facts.
  2. concepts.
  3. procedures.
  4. principles.

18. Consider a binary classification problem. Suppose I have trained a model on a linearly separable training set, and now I get a new labeled data point which is correctly classified by the model, and far away from the decision boundary. If I now add this new point to my earlier training set and re-train, in which cases is the learnt decision boundary likely to change?

  1. When my model is a perceptron and logistic regression.
  2. When my model is logistic regression and Gaussian discriminant analysis.
  3. When my model is an SVM.
  4. When my model is a perceptron

19. Data used to build a data mining model.

  1. validation data
  2. training data
  3. test data
  4. hidden data

20. Data used to optimize the parameter settings of a supervised learner model.

  1. training
  2. test
  3. verification
  4. validation

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