730+ Machine Learning (ML) Solved MCQs

Machine learning is a subset of artificial intelligence that involves the use of algorithms and statistical models to enable a system to improve its performance on a specific task over time. In other words, machine learning algorithms are designed to allow a computer to learn from data, without being explicitly programmed.

These multiple-choice questions (MCQs) are designed to enhance your knowledge and understanding in the following areas: Computer Science Engineering (CSE) .

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151.	The number of iterations in apriori ___________ Select one: a. b. c. d.
A.	increases with the size of the data
B.	decreases with the increase in size of the data
C.	increases with the size of the maximum frequent set
D.	decreases with increase in size of the maximum frequent set
Answer» C. increases with the size of the maximum frequent set

152.	Frequent item sets is
A.	superset of only closed frequent item sets
B.	superset of only maximal frequent item sets
C.	subset of maximal frequent item sets
D.	superset of both closed frequent item sets and maximal frequent item sets
Answer» D. superset of both closed frequent item sets and maximal frequent item sets

153.	A good clustering method will produce high quality clusters with
A.	high inter class similarity
B.	low intra class similarity
C.	high intra class similarity
D.	no inter class similarity
Answer» C. high intra class similarity

154.	Which statement is true about neural network and linear regression models?
A.	both techniques build models whose output is determined by a linear sum of weighted input attribute values
B.	the output of both models is a categorical attribute value
C.	both models require numeric attributes to range between 0 and 1
D.	both models require input attributes to be numeric
Answer» D. both models require input attributes to be numeric

155.	Which Association Rule would you prefer
A.	high support and medium confidence
B.	high support and low confidence
C.	low support and high confidence
D.	low support and low confidence
Answer» C. low support and high confidence

156.	In a Rule based classifier, If there is a rule for each combination of attribute values, what do you called that rule set R
A.	exhaustive
B.	inclusive
C.	comprehensive
D.	mutually exclusive
Answer» A. exhaustive

157.	The apriori property means
A.	if a set cannot pass a test, its supersets will also fail the same test
B.	to decrease the efficiency, do level-wise generation of frequent item sets
C.	to improve the efficiency, do level-wise generation of frequent item sets d.
D.	if a set can pass a test, its supersets will fail the same test
Answer» A. if a set cannot pass a test, its supersets will also fail the same test

158.	If an item set ‘XYZ’ is a frequent item set, then all subsets of that frequent item set are
A.	undefined
B.	not frequent
C.	frequent
D.	can not say
Answer» C. frequent

159.	Clustering is ___________ and is example of ____________learning
A.	predictive and supervised
B.	predictive and unsupervised
C.	descriptive and supervised
D.	descriptive and unsupervised
Answer» D. descriptive and unsupervised

160.	To determine association rules from frequent item sets
A.	only minimum confidence needed
B.	neither support not confidence needed
C.	both minimum support and confidence are needed
D.	minimum support is needed
Answer» C. both minimum support and confidence are needed

161.	If {A,B,C,D} is a frequent itemset, candidate rules which is not possible is
A.	c –> a
B.	d –>abcd
C.	a –> bc
D.	b –> adc
Answer» B. d –>abcd

163.	This clustering algorithm terminates when mean values computed for the current iteration of the algorithm are identical to the computed mean values for the previous iteration
A.	conceptual clustering
B.	k-means clustering
C.	expectation maximization
D.	agglomerative clustering
Answer» B. k-means clustering

164.	Classification rules are extracted from _____________
A.	decision tree
B.	root node
C.	branches
D.	siblings
Answer» A. decision tree

165.	What does K refers in the K-Means algorithm which is a non-hierarchical clustering approach?
A.	complexity
B.	fixed value
C.	no of iterations
D.	number of clusters
Answer» D. number of clusters

166.	How will you counter over-fitting in decision tree?
A.	by pruning the longer rules
B.	by creating new rules
C.	both by pruning the longer rules’ and ‘ by creating new rules’
D.	none of the options
Answer» A. by pruning the longer rules

162.	Which Association Rule would you prefer
A.	high support and low confidence
B.	low support and high confidence
C.	low support and low confidence
D.	high support and medium confidence
Answer» B. low support and high confidence

167.	What are two steps of tree pruning work?
A.	pessimistic pruning and optimistic pruning
B.	postpruning and prepruning
C.	cost complexity pruning and time complexity pruning
D.	none of the options
Answer» B. postpruning and prepruning

168.	Which of the following sentences are true?
A.	in pre-pruning a tree is \pruned\ by halting its construction early
B.	a pruning set of class labelled tuples is used to estimate cost complexity
C.	the best pruned tree is the one that minimizes the number of encoding bits
D.	all of the above
Answer» D. all of the above

169.	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
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

170.	Which of the following properties are characteristic of decision trees? (a) High bias (b) High variance (c) Lack of smoothness of prediction surfaces (d) Unbounded parameter set
A.	a and b
B.	a and d
C.	b, c and d
D.	all of the above
Answer» C. b, c and d

171.	To control the size of the tree, we need to control the number of regions. One approach to do this would be to split tree nodes only if the resultant decrease in the sum of squares error exceeds some threshold. For the described method, which among the following are true? (a) It would, in general, help restrict the size of the trees (b) It has the potential to affect the performance of the resultant regression/classification model (c) It is computationally infeasible
A.	a and b
B.	a and d
C.	b, c and d
D.	all of the above
Answer» A. a and b

172.	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
Answer» D. identify the model which gives performance close to the best greedy approximation performance (option (b)) with the smallest partition scheme

173.	Having built a decision tree, we are using reduced error pruning to reduce the size of the tree. We select a node to collapse. For this particular node, on the left branch, there are 3 training data points with the following outputs: 5, 7, 9.6 and for the right branch, there are four training data points with the following outputs: 8.7, 9.8, 10.5, 11. What were the original responses for data points along the two branches (left & right respectively) and what is the new response after collapsing the node?
A.	10.8, 13.33, 14.48
B.	10.8, 13.33, 12.06
C.	7.2, 10, 8.8
D.	7.2, 10, 8.6
Answer» C. 7.2, 10, 8.8

174.	Suppose on performing reduced error pruning, we collapsed a node and observed an improvement in the prediction accuracy on the validation set. Which among the following statements are possible in light of the performance improvement observed? (a) The collapsed node helped overcome the effect of one or more noise affected data points in the training set (b) The validation set had one or more noise affected data points in the region corresponding to the collapsed node (c) The validation set did not have any data points along at least one of the collapsed branches (d) The validation set did have data points adversely affected by the collapsed node
A.	a and b
B.	a and d
C.	b, c and d
D.	all of the above
Answer» D. all of the above

175.	Time Complexity of k-means is given by
A.	o(mn)
B.	o(tkn)
C.	o(kn)
D.	o(t2kn)
Answer» B. o(tkn)

730+ Machine Learning (ML) Solved MCQs

The number of iterations in apriori ___________ Select one: a. b. c. d.

Frequent item sets is

A good clustering method will produce high quality clusters with

Which statement is true about neural network and linear regression models?

Which Association Rule would you prefer

In a Rule based classifier, If there is a rule for each combination of attribute values, what do you called that rule set R

The apriori property means

If an item set ‘XYZ’ is a frequent item set, then all subsets of that frequent item set are

Clustering is ___________ and is example of ____________learning

To determine association rules from frequent item sets

If {A,B,C,D} is a frequent itemset, candidate rules which is not possible is

Which Association Rule would you prefer

This clustering algorithm terminates when mean values computed for the current iteration of the algorithm are identical to the computed mean values for the previous iteration

Classification rules are extracted from _____________

What does K refers in the K-Means algorithm which is a non-hierarchical clustering approach?

How will you counter over-fitting in decision tree?

What are two steps of tree pruning work?

Which of the following sentences are true?

Which of the following properties are characteristic of decision trees? (a) High bias (b) High variance (c) Lack of smoothness of prediction surfaces (d) Unbounded parameter set

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

Time Complexity of k-means is given by

In Apriori algorithm, if 1 item-sets are 100, then the number of candidate 2 item-sets are

Machine learning techniques differ from statistical techniques in that machine learning methods

What is the final resultant cluster size in Divisive algorithm, which is one of the hierarchical clustering approaches?

Given a frequent itemset L, If |L| = k, then there are

Which Statement is not true statement.

which of the following cases will K-Means clustering give poor results? 1. Data points with outliers 2. Data points with different densities 3. Data points with round shapes 4. Data points with non-convex shapes

What is Decision Tree?

What are two steps of tree pruning work?

A database has 5 transactions. Of these, 4 transactions include milk and bread. Further, of the given 4 transactions, 2 transactions include cheese. Find the support percentage for the following association rule “if milk and bread are purchased, then cheese is also purchased”.

Which of the following option is true about k-NN algorithm?

How to select best hyperparameters in tree based models?

What is true about K-Mean Clustering? 1. K-means is extremely sensitive to cluster center initializations 2. Bad initialization can lead to Poor convergence speed 3. Bad initialization can lead to bad overall clustering

What are tree based classifiers?

What is gini index?

Tree/Rule based classification algorithms generate ... rule to perform the classification.

Decision Tree is

Which of the following is true about Manhattan distance?

hich of the following classifications would best suit the student performance classification systems?

Which statement is true about the K-Means algorithm? Select one:

In which of the following cases will K-means clustering fail to give good results? 1) Data points with outliers 2) Data points with different densities 3) Data points with nonconvex shapes

How will you counter over-fitting in decision tree?

Clustering is _ and is example of __learning

Which of the following properties are characteristic of decision trees?
(a) High bias
(b) High variance
(c) Lack of smoothness of prediction surfaces
(d) Unbounded parameter set

which of the following cases will K-Means clustering give poor results?
1. Data points with outliers
2. Data points with different densities
3. Data points with round shapes
4. Data points with non-convex shapes

What is true about K-Mean Clustering?
1. K-means is extremely sensitive to cluster center initializations
2. Bad initialization can lead to Poor convergence speed
3. Bad initialization can lead to bad overall clustering

176.	In Apriori algorithm, if 1 item-sets are 100, then the number of candidate 2 item-sets are
A.	100
B.	200
C.	4950
D.	5000
Answer» C. 4950

177.	Machine learning techniques differ from statistical techniques in that machine learning methods
A.	are better able to deal with missing and noisy data
B.	typically assume an underlying distribution for the data
C.	have trouble with large-sized datasets
D.	are not able to explain their behavior
Answer» A. are better able to deal with missing and noisy data

178.	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 donâ€™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.	0.0368
B.	0.0396
C.	0.0389
D.	0.0398
Answer» B. 0.0396

179.	What is the final resultant cluster size in Divisive algorithm, which is one of the hierarchical clustering approaches?
A.	zero
B.	three
C.	singleton
D.	two
Answer» C. singleton

180.	Given a frequent itemset L, If \|L\| = k, then there are
A.	2k – 1 candidate association rules
B.	2k candidate association rules
C.	2k – 2 candidate association rules
D.	2k -2 candidate association rules
Answer» C. 2k – 2 candidate association rules

181.	Which Statement is not true statement.
A.	k-means clustering is a linear clustering algorithm.
B.	k-means clustering aims to partition n observations into k clusters
C.	k-nearest neighbor is same as k-means
D.	k-means is sensitive to outlier
Answer» B. k-means clustering aims to partition n observations into k clusters

182.	which of the following cases will K-Means clustering give poor results? 1. Data points with outliers 2. Data points with different densities 3. Data points with round shapes 4. Data points with non-convex shapes
A.	1 and 2
B.	2 and 3
C.	2 and 4
D.	1, 2 and 4
Answer» C. 2 and 4

183.	What is Decision Tree?
A.	flow-chart
B.	structure in which internal node represents test on an attribute, each branch represents outcome of test and each leaf node represents class label
C.	flow-chart like structure in which internal node represents test on an attribute, each branch represents outcome of test and each leaf node represents class label
D.	none of the above
Answer» D. none of the above

184.	What are two steps of tree pruning work?
A.	pessimistic pruning and optimistic pruning
B.	postpruning and prepruning
C.	cost complexity pruning and time complexity pruning
D.	none of the options
Answer» B. postpruning and prepruning

185.	A database has 5 transactions. Of these, 4 transactions include milk and bread. Further, of the given 4 transactions, 2 transactions include cheese. Find the support percentage for the following association rule “if milk and bread are purchased, then cheese is also purchased”.
A.	0.4
B.	0.6
C.	0.8
D.	0.42
Answer» D. 0.42