Machine Learning MCQ Questions with answers and solutions Page 3

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Q. what is the function of 'Supervised Learning'?

(A) classifications, predict time series, annotate strings
(B) 2 and 3
(C) both a and b
(D) none of above

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Q. Suppose you are training a linear regression model. Now consider these points.
1. Overfitting is more likely if we have less data
2. Overfitting is more likely when the hypothesis space is small.Which of the above statement(s) are correct?

(A) both are false
(B) 1 is false and 2 is true
(C) 1 is true and 2 is false
(D) both are true

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Mr. Dubey • 52.61K Points
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Q. Gini index does not favour equal sized partitions.

(A) TRUE
(B) Find interesting directions in data and find novel observations/ database cleaning
(C) ---
(D) ---

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Mr. Dubey • 52.61K Points
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Q. what is the function of 'Unsupervised Learning'?

(A) Find clusters of the data and find low-dimensional representations of the data
(B) boosting
(C) Interesting coordinates and correlations
(D) All

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Mr. Dubey • 52.61K Points
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Q. In an election, N candidates are competing against each other and people are voting for either of the candidates. Voters don't communicate with each other while casting their votes. Which of the following ensemble method works similar to above-discussed election procedure? Hint: Persons are like base models of ensemble method.

(A) bagging
(B) the data is noisy and contains overlapping points
(C) a or b
(D) none of these

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Mr. Dubey • 52.61K Points
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Q. The soft margin SVM is more preferred than the hard-margin SVM when-

(A) the data is linearly seperable
(B) The relationship is not symmetric between x and y in both.
(C) the data is not noisy and linearly seperable
(D) the data is noisy and linearly seperable

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Mr. Dubey • 52.61K Points
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Q. Which of the following option is true regarding "Regression" and "Correlation"?
Note: y is dependent variable and x is independent variable.

(A) The relationship is symmetric between x and y in both.
(B) when irrelevant attributes have been removed from the data.
(C) The relationship is not symmetric between x and y in case of correlation but in case of regression it is symmetric.
(D) The relationship is symmetric between x and y in case of correlation but in case of regression it is not symmetric.

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Mr. Dubey • 52.61K Points
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Q. A nearest neighbor approach is best used

(A) with large-sized datasets.
(B) 0.26
(C) when a generalized model of the data is desirable.
(D) when an explanation of what has been found is of primary importance.

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Mr. Dubey • 52.61K Points
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Q. For the given weather data, what is the probability that players will play if weather is sunny

(A) 0.5
(B) density-based clustering
(C) 0.73
(D) 0.6

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Mr. Dubey • 52.61K Points
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Q. Suppose we would like to perform clustering on spatial data such as the geometrical locations of houses. We wish to produce clusters of many different sizes and shapes. Which of the following methods is the most appropriate?

(A) decision trees
(B) to save space for storing the decision tree
(C) model-based clustering
(D) k-means clustering

Machine Learning MCQs with answers Page - 3

Q. what is the function of 'Supervised Learning'?

Q. Suppose you are training a linear regression model. Now consider these points. 1. Overfitting is more likely if we have less data 2. Overfitting is more likely when the hypothesis space is small.Which of the above statement(s) are correct?

Q. Gini index does not favour equal sized partitions.

Q. what is the function of 'Unsupervised Learning'?

Q. The soft margin SVM is more preferred than the hard-margin SVM when-

Q. Which of the following option is true regarding "Regression" and "Correlation"? Note: y is dependent variable and x is independent variable.

Q. A nearest neighbor approach is best used

Q. For the given weather data, what is the probability that players will play if weather is sunny

Q. Suppose we would like to perform clustering on spatial data such as the geometrical locations of houses. We wish to produce clusters of many different sizes and shapes. Which of the following methods is the most appropriate?

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