Machine Learning MCQ Questions with answers and solutions Page 6

Q. Assume that you are given a data set and a neural network model trained on the data set. You are asked to build a decision tree model with the sole purpose of understanding/interpreting the built neural network model. In such a scenario, which among the following measures would you concentrate most on optimising?

(A) accuracy of the decision tree model on the given data set
(B) f1 measure of the decision tree model on the given data set
(C) fidelity of the decision tree model, which is the fraction of instances on which the neural network and the decision tree give the same output
(D) comprehensibility of the decision tree model, measured in terms of the size of the corresponding rule set

✅ Correct Answer: (C) fidelity of the decision tree model, which is the fraction of instances on which the neural network and the decision tree give the same output

Published by Mr. Dubey ⭐ 111.54K Points at 26-Apr-2024 12:21 pm in Machine Learning

Q. What are common feature selection methods in regression task?

(A) correlation coefficient
(B) greedy algorithms
(C) all above
(D) none of these

💬 Discuss

✅ Correct Answer: (C) all above

Published by Mr. Dubey ⭐ 111.54K Points at 26-Apr-2024 12:21 pm in Machine Learning

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

💬 Discuss

✅ Correct Answer: (B) ii and iii

Published by Mr. Dubey ⭐ 111.54K Points at 26-Apr-2024 12:21 pm in Machine Learning

Q. Which of the following can only be used when training data are linearlyseparable?

(A) linear hard-margin svm
(B) linear logistic regression
(C) linear soft margin svm
(D) the centroid method

💬 Discuss

✅ Correct Answer: (A) linear hard-margin svm

Published by Mr. Dubey ⭐ 111.54K Points at 26-Apr-2024 12:21 pm in Machine Learning

Q. Wrapper methods are hyper-parameter selection methods that

(A) should be used whenever possible because they are computationally efficient
(B) should be avoided unless there are no other options because they are always prone to overfitting.
(C) are useful mainly when the learning machines are "black boxes"
(D) should be avoided altogether.

💬 Discuss

✅ Correct Answer: (C) are useful mainly when the learning machines are "black boxes"

Published by Mr. Dubey ⭐ 111.54K Points at 26-Apr-2024 12:21 pm in Machine Learning

Q. Given that we can select the same feature multiple times during the recursive partitioning of the input space, is it always possible to achieve 100% accuracy on the training data (given that we allow for trees to grow to their maximum size) when building decision trees?

(A) Yes
(B) 2
(C) ---
(D) ---

💬 Discuss

✅ Correct Answer: (B) 2

Published by Mr. Dubey ⭐ 111.54K Points at 26-Apr-2024 12:21 pm in Machine Learning

Q. What characterize unlabeled examples in machine learning

(A) there is no prior knowledge
(B) there is no confusing knowledge
(C) there is prior knowledge
(D) there is plenty of confusing knowledge

💬 Discuss

✅ Correct Answer: (A) there is no prior knowledge

Explanation: What characterizes unlabeled examples in machine learning?

Published by Mr. Dubey ⭐ 111.54K Points at 26-Apr-2024 12:21 pm in Machine Learning

Q. What characterize is hyperplance in geometrical model of machine learning?

(A) a plane with 1 dimensional fewer than number of input attributes
(B) a plane with 2 dimensional fewer than number of input attributes
(C) a plane with 1 dimensional more than number of input attributes
(D) a plane with 2 dimensional more than number of input attributes

💬 Discuss

✅ Correct Answer: (A) a plane with 1 dimensional fewer than number of input attributes

Explanation: What characterizes a hyperplane in the geometrical model of machine learning?

Published by Mr. Dubey ⭐ 111.54K Points at 26-Apr-2024 12:21 pm in Machine Learning

Q. Imagine a Newly-Born starts to learn walking. It will try to find a suitable policy to learn walking after repeated falling and getting up.specify what type of machine learning is best suited?

(A) classification
(B) regression
(C) kmeans algorithm
(D) reinforcement learning

💬 Discuss

✅ Correct Answer: (D) reinforcement learning

Explanation: Imagine a newly-born starts to learn walking. It will try to find a suitable policy to learn walking after repeated falling and getting up. Specify what type of machine learning is best suited?

Published by Mr. Dubey ⭐ 111.54K Points at 26-Apr-2024 12:21 pm in Machine Learning

Q. What are the popular algorithms of Machine Learning?

(A) decision trees and neural networks (back propagation)
(B) probabilistic networks and nearest neighbor
(C) support vector machines
(D) all

💬 Discuss

✅ Correct Answer: (D) all

Explanation: What are the popular algorithms of Machine Learning?

Published by Mr. Dubey ⭐ 111.54K Points at 26-Apr-2024 12:21 pm in Machine Learning

Machine Learning MCQs with answers Page - 6

Q. What are common feature selection methods in regression task?

Q. Which of the following can only be used when training data are linearlyseparable?

Q. Wrapper methods are hyper-parameter selection methods that

Q. Given that we can select the same feature multiple times during the recursive partitioning of the input space, is it always possible to achieve 100% accuracy on the training data (given that we allow for trees to grow to their maximum size) when building decision trees?

Q. What characterize unlabeled examples in machine learning

Q. What characterize is hyperplance in geometrical model of machine learning?

Q. Imagine a Newly-Born starts to learn walking. It will try to find a suitable policy to learn walking after repeated falling and getting up.specify what type of machine learning is best suited?

Q. What are the popular algorithms of Machine Learning?

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