Machine Learning MCQ Questions with answers and solutions Page 4

Q. What would you do in PCA to get the same projection as SVD?

(A) transform data to zero mean
(B) transform data to zero median
(C) not possible
(D) none of these

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✅ Correct Answer: (A) transform data to zero mean

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

Q. The . . . . . . . . of the hyperplane depends upon the number of features.

(A) dimension
(B) classification
(C) reduction
(D) none of the above

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✅ Correct Answer: (A) dimension

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

Q. What is the approach of basic algorithm for decision tree induction?

(A) greedy
(B) top down
(C) procedural
(D) step by step

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✅ Correct Answer: (A) greedy

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

Q. Can we extract knowledge without apply feature selection

(A) Yes
(B) 0.06
(C) ---
(D) ---

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✅ Correct Answer: (A) Yes

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

Q. Suppose there are 25 base classifiers. Each classifier has error rates of e = 0.35. Suppose you are using averaging as ensemble technique. What will be the probabilities that ensemble of above 25 classifiers will make a wrong prediction? Note: All classifiers are independent of each other

(A) 0.05
(B) validation data
(C) 0.07
(D) 0.09

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✅ Correct Answer: (B) validation data

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

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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✅ Correct Answer: (A) TRUE

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

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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✅ Correct Answer: (A) training data

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

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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✅ Correct Answer: (B) false - perceptrons are mathematically incapable of solving linearly inseparable functions, no matter what you do

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

Q. Computers are best at learning

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

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✅ Correct Answer: (A) facts.

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

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

💬 Discuss

✅ Correct Answer: (A) in distance calculation it will give the same weights for all features

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

Machine Learning MCQs with answers Page - 4

Q. What would you do in PCA to get the same projection as SVD?

Q. The . . . . . . . . of the hyperplane depends upon the number of features.

Q. What is the approach of basic algorithm for decision tree induction?

Q. Can we extract knowledge without apply feature selection

Q. Suppose there are 25 base classifiers. Each classifier has error rates of e = 0.35. Suppose you are using averaging as ensemble technique. What will be the probabilities that ensemble of above 25 classifiers will make a wrong prediction? Note: All classifiers are independent of each other

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

Q. Data used to build a data mining model.

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

Q. Computers are best at learning

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

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