Machine Learning MCQs with answers Page - 14

Here, you will find a collection of MCQ questions on Machine Learning. Go through these questions to enhance your preparation for upcoming examinations and interviews.

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Q. Can we calculate the skewness of variables based on mean and median?

  • (A) TRUE
  • (B) Test error is non zero
  • (C) ---
  • (D) ---

A

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Q. It is possible to design a Linear regression algorithm using a neural network?

  • (A) TRUE
  • (B) b and c
  • (C) ---
  • (D) ---

A

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Q. Increase in size of a convolutional kernel would necessarily increase the performance of a convolutional network.

  • (A) TRUE
  • (B) linear, binary
  • (C) ---
  • (D) ---

A

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Q. To control the size of the tree, we need to control the number of regions. One approach to do this would be to split tree nodes only if the resultant decrease in the sum of squares error exceeds some threshold. For the described method, which among the following are true?
a. It would, in general, help restrict the size of the trees
b. It has the potential to affect the performance of the resultant regression/classification model
c. It is computationally infeasible

  • (A) a and b
  • (B) unsupervised learning
  • (C) a and c
  • (D) all of the above

A

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Q. Which of the following is the difference between stacking and blending?

  • (A) stacking has less stable cv compared to blending
  • (B) backword feature selection
  • (C) stacking is simpler than blending
  • (D) none of these

A

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Q. Logistic regression is a . . . . . . . . regression technique that is used to model data having a . . . . . . . . outcome.

  • (A) linear, numeric
  • (B) nonlinear functions
  • (C) nonlinear, numeric
  • (D) nonlinear, binary

A

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Q. How can you avoid overfitting ?

  • (A) by using a lot of data
  • (B) by using inductive machine learning
  • (C) by using validation only
  • (D) none of above

A

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Q. What are the steps for using a gradient descent algorithm?
1. Calculate error between the actual value and the predicted value
2. Reiterate until you find the best weights of network
3. Pass an input through the network and get values from output layer
4. Initialize random weight and bias
5. Go to each neurons which contributes to the error and change its respective values to reduce the error

  • (A) 1, 2, 3, 4, 5
  • (B) 4, 3, 1, 5, 2
  • (C) 3, 2, 1, 5, 4
  • (D) 5, 4, 3, 2, 1

A

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Q. Problem in multi regression is ?

  • (A) multicollinearity
  • (B) overfitting
  • (C) both multicollinearity & overfitting
  • (D) underfitting

A

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Q. Tree/Rule based classification algorithms generate ... rule to perform the classification.

  • (A) if-then.
  • (B) while.
  • (C) do while
  • (D) switch.

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