Machine Learning: MCQs Set – 19

Machine Learning: MCQs Set – 19

Q181: Any hypothesis find an approximation of the target function over a sufficiently large set of training examples will also approximate the target function well over other unobserved examples. This is called _____.

  • (A) Hypothesis
  • (B) Inductive hypothesis
  • (C) Learning
  • (D) Concept learning

Q182: Principal component is a technique for

  • (A) Feature selection
  • (B) Dimensionality reduction
  • (C) Exploration
  • (D) None of the above

Q183: Data can broadly divided into following two types

  • (A) Qualitative
  • (B) Speculative
  • (C) Quantitative
  • (D) None of the above

Q184: Out of 200 emails, a classification model correctly predicted 150 spam emails and 30 ham emails. What is the accuracy of the model?

  • (A) 10%
  • (B) 90%
  • (C) 80%
  • (D) none of the above

Q185: In feature extraction, some of the commonly used ___ are used for combining the original features.

  • (A) Operators
  • (B) Delimiters
  • (C) Words
  • (D) All of the above

Q186: This type of interpretation of probability tries to quantify the uncertainty of some event and thus focuses on information rather than repeated trials.

  • (A) Frequency interpretation of probability
  • (B) Gaussian interpretation of probability
  • (C) Machine learning interpretation of probability
  • (D) Bayesian interpretation of probability

Q187: The probability that a particular hypothesis holds for a data set based on the Prior is called

  • (A) Independent probabilities
  • (B) Posterior probabilities
  • (C) Interior probabilities
  • (D) Dependent probabilities

Q188: Which is a type of machine learning where a target feature, which is of categorical type, is predicted for the test data on the basis of the information imparted by the training data?

  • (A) Unsupervised Learning
  • (B) Supervised Regression
  • (C) Supervised Classification
  • (D) Categorical Attribute

Q189: ———- are the data points (representing classes), the important component in a data set, which are near the identified set of lines (hyperplane).

  • (A) Hard Margin
  • (B) Soft Margin
  • (C) Linear Margin
  • (D) Support Vectors

Q190: In SVM, these functions take a lower dimensional input space and transform it to a higher dimensional space.

  • (A) Kernels
  • (B) Vector
  • (C) Support Vector
  • (D) Hyperplane