Q. Regarding bias and variance, which of the following statements are true? (Here 'high' and 'low' are relative to the ideal model. (i) Models which overfit are more likely to have high bias (ii) Models which overfit are more likely to have low bias (iii) Models which overfit are more likely to have high variance (iv) Models which overfit are more likely to have low variance

  • (A) (i) and (ii)
  • (B) (ii) and (iii)
  • (C) (iii) and (iv)
  • (D) none of these
πŸ’¬ Discuss
βœ… Correct Answer: (B) (ii) and (iii)

Q. Selecting data so as to assure that each class is properly represented in both the training and test set.

  • (A) cross validation
  • (B) stratification
  • (C) verification
  • (D) bootstrapping
πŸ’¬ Discuss
βœ… Correct Answer: (B) stratification

Q. Which of the following scale data by removing elements that don't belong to a given range or by considering a maximum absolute value.

  • (A) MinMaxScaler
  • (B) MaxAbsScaler
  • (C) Both A and B
  • (D) None of the Mentioned
πŸ’¬ Discuss
βœ… Correct Answer: (C) Both A and B

Q. Which of the following selects the best K high-score features.

  • (A) selectpercentile
  • (B) featurehasher
  • (C) selectkbest
  • (D) all above
πŸ’¬ Discuss
βœ… Correct Answer: (C) selectkbest

Q. Which of the following statements are true for a design matrix X ∈ Rn×d with d > n? (The rows are n sample points and the columns represent d features.)

  • (A) least-squares linear regression computes the weights w = (xtx)-1 xty
  • (B) the sample points are linearly separable
  • (C) x has exactly d - n eigenvectors with eigenvalue zero
  • (D) at least one principal component direction is orthogonal to a hyperplane that contains all the sample points
πŸ’¬ Discuss
βœ… Correct Answer: (D) at least one principal component direction is orthogonal to a hyperplane that contains all the sample points

Q. Attribute selection measures are also known as splitting rules.

  • (A) TRUE
  • (B) FeatureHasher
  • (C) ---
  • (D) ---
πŸ’¬ Discuss
βœ… Correct Answer: (A) TRUE

Q. A 4-input neuron has weights 1, 2, 3 and 4. The transfer function is linear with the constant of proportionality being equal to 2. The inputs are 4, 10, 10 and 30 respectively. What will be the output?

  • (A) 238
  • (B) 76
  • (C) 248
  • (D) 348
πŸ’¬ Discuss
βœ… Correct Answer: (D) 348

Q. Suppose, you want to apply a stepwise forward selection method for choosing the best models for an ensemble model. Which of the following is the correct order of the steps? Note: You have more than 1000 models predictions.
1. Add the models predictions (or in another term take the average) one by one in the ensemble which improves the metrics in the validation set.
2. Start with empty ensemble
3. Return the ensemble from the nested set of ensembles that has maximum performance on the validation set

  • (A) 01-02-03
  • (B) 01-03-04
  • (C) 02-01-03
  • (D) none of above
πŸ’¬ Discuss
βœ… Correct Answer: (D) none of above

Q. Which of the following metrics can be used for evaluating regression models?
1. R Squared
2. Adjusted R Squared
3. F Statistics
4. RMSE / MSE / MAE

  • (A) 2 and 4
  • (B) 1 and 2
  • (C) 2, 3 and 4
  • (D) 1, 2, 3 and 4
πŸ’¬ Discuss
βœ… Correct Answer: (D) 1, 2, 3 and 4

Q. What do you mean by generalization error in terms of the SVM?

  • (A) how far the hyperplane is from the support vectors
  • (B) how accurately the svm can predict outcomes for unseen data
  • (C) the threshold amount of error in an svm
  • (D) None of these
πŸ’¬ Discuss
βœ… Correct Answer: (B) how accurately the svm can predict outcomes for unseen data