Machine Learning MCQs with answers Page - 10

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Q. With Bayes classifier, missing data items are

  • (A) treated as equal compares.
  • (B) treated as unequal compares.
  • (C) replaced with a default value.
  • (D) ignored.

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Q. scikit-learn also provides functions for creating dummy datasets from scratch:

  • (A) make_classification()
  • (B) make_regression()
  • (C) make_blobs()
  • (D) all above

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Q. What is the actual number of independent parameters which need to be estimated in P dimensional Gaussian distribution model?

  • (A) p
  • (B) 2p
  • (C) p(p+1) 2
  • (D) p(p+3) 2

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Q. Solving a non linear separation problem with a hard margin Kernelized SVM (Gaussian RBF Kernel) might lead to overfitting

  • (A) TRUE
  • (B) high support and low confidence
  • (C) ---
  • (D) ---

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Q. Which Association Rule would you prefer

  • (A) high support and medium confidence
  • (B) input attributes to be categorical.
  • (C) low support and high confidence
  • (D) low support and low confidence

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Q. Supervised learning differs from unsupervised clustering in that supervised learning requires

  • (A) at least one input attribute.
  • (B) predictive model
  • (C) at least one output attribute.
  • (D) output attributes to be categorical.

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Q. Type of matrix decomposition model is

  • (A) descriptive model
  • (B) Bias decreases and Variance increases
  • (C) logical model
  • (D) none of the above

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Q. Bayes Theorem is given by where
1. P(H) is the probability of hypothesis H being true.
2. P(E) is the probability of the evidence(regardless of the hypothesis).
3. P(E|H) is the probability of the evidence given that hypothesis is true.
4. P(H|E) is the probability of the hypothesis given that the evidence is there.

  • (A) TRUE
  • (B) Higher is better
  • (C) ---
  • (D) ---

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Q. We have been given a dataset with n records in which we have input attribute as x and output attribute as y. Suppose we use a linear regression method to model this data. To test our linear regressor, we split the data in training set and test set randomly. What do you expect will happen with bias and variance as you increase the size of training data?

  • (A) Bias increases and Variance increases
  • (B) discipline in statistics used to find projections in multidimensional data
  • (C) Bias decreases and Variance decreases
  • (D) Bias increases and Variance decreases

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Q. Which of the following is true about Residuals ?

  • (A) Lower is better
  • (B) lr(formula, data)
  • (C) A or B depend on the situation
  • (D) None of these

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