Machine Learning: MCQs Set – 16

Q151: __________ has been used to train vehicles to steer correctly and autonomously on road.

  • (A) Machine learning
  • (B) Data mining
  • (C) Robotics
  • (D) Neural networks

Q152: This type of learning to be used when there is no idea about the class or label of a particular data

  • (A) Supervised learning algorithm
  • (B) Unsupervised learning algorithm
  • (C) Semi-supervised learning algorithm
  • (D) Reinforcement learning algorithm

Q153: For understanding relationship between two variables, ____ can be used.

  • (A) Box plot
  • (B) Scatter plot
  • (C) Histogram
  • (D) None of the above

Q154: Feature ___ involves transforming a given set of input features to generate a new set of more powerful features.

  • (A) Selection
  • (B) Engineering
  • (C) Transformation
  • (D) Re-engineering

Q155: This approach is quite similar to wrapper approach as it also uses and inductive algorithm to evaluate the generated feature subsets.

  • (A) Embedded approach
  • (B) Filter approach
  • (C) Pro Wrapper approach
  • (D) Hybrid approach
  • (A) Cov(X,Y) = E(XY)−E(X)E(Y)
  • (B) Cov(X,Y) = E(XY)+ E(X)E(Y)
  • (C) Cov(X,Y) = E(XY)/E(X)E(Y)
  • (D) Cov(X,Y) = E(X)E(Y)/ E(XY)

Q157: Training data run on the algorithm is called as?

  • (A) Program
  • (B) Training
  • (C) Training Information
  • (D) Learned Function

Q158: What would be the relationship between the training time taken by 1-NN, 2-NN, and 3-NN?

  • (A) 1-NN > 2-NN > 3-NN
  • (B) 1-NN < 2-NN < 3-NN
  • (C) 1-NN ~ 2-NN ~ 3-NN
  • (D) None of these

Q159: Which of the following algorithms is an example of the ensemble learning algorithm?

  • (A) Random Forest
  • (B) Decision Tree
  • (C) NN
  • (D) SVM

Q160: Which of the following is not an inductive bias in a decision tree?

  • (A) It prefers longer tree over shorter tree
  • (B) Trees that place nodes near the root with high information gain are preferred
  • (C) Overfitting is a natural phenomenon in a decision tree
  • (D) Prefer the shortest hypothesis that fits the data



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