Machine Learning MCQ Questions: Practice and Test Your Knowledge Page-4 section-1

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31.
Computers are best at learning

A. facts.

B. concepts.

C. procedures.

D. principles.

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32.
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

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33.
Which of the following is true about Naive Bayes?

A. Assumes that all the features in a dataset are equally important

B. Assumes that all the features in a dataset are independent

C. Both A and B

D. None of the above option

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34.
KDD represents extraction of

A. data

B. knowledge

C. rules

D. model

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35.
Linear Regression is a . . . . . . . . machine learning algorithm.

A. supervised

B. unsupervised

C. semi-supervised

D. cant say

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36.
The probability that a person owns a sports car given that they subscribe to automotive magazine is 40%. We also know that 3% of the adult population subscribes to automotive magazine. The probability of a person owning a sports car given that they don't subscribe to automotive magazine is 30%. Use this information to compute the probability that a person subscribes to automotive magazine given that they own a sports car

A. 0.0398

B. 0.0389

C. 0.0368

D. 0.0396

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37.
Which among the following statements best describes our approach to learning decision trees

A. identify the best partition of the input space and response per partition to minimise sum of squares error

B. identify the best approximation of the above by the greedy approach (to identifying the partitions)

C. identify the model which gives the best performance using the greedy approximation (option (b)) with the smallest partition scheme

D. identify the model which gives performance close to the best greedy approximation performance (option (b)) with the smallest partition scheme

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38.
Which of the following techniques would perform better for reducing dimensions of a data set?

A. removing columns which have too many missing values

B. removing columns which have high variance in data

C. removing columns with dissimilar data trends

D. none of these

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39.
. . . . . . . . can be adopted when it's necessary to categorize a large amount of data with a few complete examples or when there's the need to impose some constraints to a clustering algorithm.

A. Supervised

B. Semi-supervised

C. Reinforcement

D. Clusters

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40.
Binarize parameter in BernoulliNB scikit sets threshold for binarizing of sample features.

A. TRUE

B. FALSE

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Machine Learning MCQ Questions: Practice and Test Your Knowledge

31. Computers are best at learning

Answer & Solution

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

Answer & Solution

33. Which of the following is true about Naive Bayes?

Answer & Solution

34. KDD represents extraction of

Answer & Solution

35. Linear Regression is a . . . . . . . . machine learning algorithm.

Answer & Solution

Answer & Solution

37. Which among the following statements best describes our approach to learning decision trees

Answer & Solution

38. Which of the following techniques would perform better for reducing dimensions of a data set?

Answer & Solution

39. . . . . . . . . can be adopted when it's necessary to categorize a large amount of data with a few complete examples or when there's the need to impose some constraints to a clustering algorithm.

Answer & Solution

40. Binarize parameter in BernoulliNB scikit sets threshold for binarizing of sample features.

Answer & Solution

Read More Section(Machine Learning)

31.
Computers are best at learning

32.
what is Feature scaling done before applying K-Mean algorithm?

33.
Which of the following is true about Naive Bayes?

34.
KDD represents extraction of

35.
Linear Regression is a . . . . . . . . machine learning algorithm.

37.
Which among the following statements best describes our approach to learning decision trees

38.
Which of the following techniques would perform better for reducing dimensions of a data set?

39.
. . . . . . . . can be adopted when it's necessary to categorize a large amount of data with a few complete examples or when there's the need to impose some constraints to a clustering algorithm.

40.
Binarize parameter in BernoulliNB scikit sets threshold for binarizing of sample features.