Machine Learning MCQs with answers Page - 4

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Q. When the number of classes is large Gini index is not a good choice.

  • (A) TRUE
  • (B) logistic regression
  • (C) ---
  • (D) ---

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Q. Data used to build a data mining model.

  • (A) training data
  • (B) to transform the problem from regression to classification
  • (C) test data
  • (D) hidden data

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Q. This technique associates a conditional probability value with each data instance.

  • (A) linear regression
  • (B) false - perceptrons are mathematically incapable of solving linearly inseparable functions, no matter what you do
  • (C) simple regression
  • (D) multiple linear regression

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Q. Computers are best at learning

  • (A) facts.
  • (B) concepts.
  • (C) procedures.
  • (D) principles.

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Q. what is Feature scaling done before applying K-Mean algorithm?

  • (A) in distance calculation it will give the same weights for all features
  • (B) you always get the same clusters. if you use or dont use feature scaling
  • (C) in manhattan distance it is an important step but in euclidian it is not
  • (D) none of these

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Q. Which of the following is true about Naive Bayes?

  • (A) Assumes that all the features in a dataset are equally important
  • (B) Assumes that all the features in a dataset are independent
  • (C) Both A and B
  • (D) None of the above option

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Q. Linear Regression is a . . . . . . . . machine learning algorithm.

  • (A) supervised
  • (B) unsupervised
  • (C) semi-supervised
  • (D) cant say

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Q. Which among the following statements best describes our approach to learning decision trees

  • (A) identify the best partition of the input space and response per partition to minimise sum of squares error
  • (B) identify the best approximation of the above by the greedy approach (to identifying the partitions)
  • (C) identify the model which gives the best performance using the greedy approximation (option (b)) with the smallest partition scheme
  • (D) identify the model which gives performance close to the best greedy approximation performance (option (b)) with the smallest partition scheme

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Q. Which of the following techniques would perform better for reducing dimensions of a data set?

  • (A) removing columns which have too many missing values
  • (B) removing columns which have high variance in data
  • (C) removing columns with dissimilar data trends
  • (D) none of these

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