Machine Learning MCQ Questions with answers and solutions Page 5

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Q. 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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Q. KDD represents extraction of

(A) data
(B) knowledge
(C) rules
(D) model

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

(A) supervised
(B) unsupervised
(C) semi-supervised
(D) cant say

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Q. 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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Q. 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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Q. 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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Q. . . . . . . . . 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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Q. The average squared difference between classifier predicted output and actual output.

(A) mean squared error
(B) root mean squared error
(C) mean absolute error
(D) mean relative error

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Correct answer - Option (A)
Explanation:

The measure described, which represents the average squared difference between the predicted output of a classifier and the actual output, is known as Option A: mean squared error. Mean squared error is a common metric used to evaluate the performance of machine learning models, with lower values indicating better predictive accuracy.

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Q. Which of the following methods do we use to find the best fit line for data in Linear Regression?

(A) Least Square Error
(B) Maximum Likelihood
(C) Logarithmic Loss
(D) Both A and B

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Correct answer - Option (A)
Explanation:

In Linear Regression, the method used to find the best fit line for data is Option A: Least Square Error. This technique minimizes the sum of the squared differences (errors) between the predicted values and the actual values in the dataset. The goal is to find the line that minimizes the overall error, making it the "best fit" line for the data.

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Q. Following are the descriptive models

(A) clustering
(B) classification
(C) association rule
(D) both a and c

Machine Learning MCQs with answers Page - 5

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

Q. KDD represents extraction of

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

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

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

Q. . . . . . . . . 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.

Q. The average squared difference between classifier predicted output and actual output.

Q. Which of the following methods do we use to find the best fit line for data in Linear Regression?

Q. Following are the descriptive models

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