Machine Learning MCQs with answers Page - 12

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📊 Machine Learning
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
💬 Discuss
✅ Correct Answer: (D) p(p+3) 2
📊 Machine Learning
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) ---
💬 Discuss
✅ Correct Answer: (A) TRUE
📊 Machine Learning
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
💬 Discuss
✅ Correct Answer: (C) low support and high confidence
📊 Machine Learning
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.
💬 Discuss
✅ Correct Answer: (B) predictive model
📊 Machine Learning
Q. Type of matrix decomposition model is
  • (A) descriptive model
  • (B) Bias decreases and Variance increases
  • (C) logical model
  • (D) none of the above
💬 Discuss
✅ Correct Answer: (A) descriptive model
📊 Machine Learning
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) ---
💬 Discuss
✅ Correct Answer: (A) TRUE
📊 Machine Learning
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
💬 Discuss
✅ Correct Answer: (D) Bias increases and Variance decreases
📊 Machine Learning
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
💬 Discuss
✅ Correct Answer: (A) Lower is better
📊 Machine Learning
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
💬 Discuss
✅ Correct Answer: (D) All of above
📊 Machine Learning
Q. For the given weather data, Calculate probability of not playing
  • (A) 0.4
  • (B) 0.64
  • (C) 0.36
  • (D) 0.5
💬 Discuss
✅ Correct Answer: (C) 0.36

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