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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1.	Application of machine learning methods to large databases is called
A.	data mining.
B.	artificial intelligence
C.	big data computing
D.	internet of things
Answer» A. data mining.

2.	If machine learning model output involves target variable then that model is called as
A.	descriptive model
B.	predictive model
C.	reinforcement learning
D.	all of the above
Answer» B. predictive model

3.	In what type of learning labelled training data is used
A.	unsupervised learning
B.	supervised learning
C.	reinforcement learning
D.	active learning
Answer» B. supervised learning

4.	In following type of feature selection method we start with empty feature set
A.	forward feature selection
B.	backword feature selection
C.	both a and b??
D.	none of the above
Answer» A. forward feature selection

5.	In PCA the number of input dimensiona are equal to principal components
A.	true
B.	false
Answer» A. true

6.	PCA can be used for projecting and visualizing data in lower dimensions.
A.	true
B.	false
Answer» A. true

7.	Which of the following is the best machine learning method?
A.	scalable
B.	accuracy
C.	fast
D.	all of the above
Answer» D. all of the above

8.	What characterize unlabeled examples in machine learning
A.	there is no prior knowledge
B.	there is no confusing knowledge
C.	there is prior knowledge
D.	there is plenty of confusing knowledge
Answer» D. there is plenty of confusing knowledge

9.	What does dimensionality reduction reduce?
A.	stochastics
B.	collinerity
C.	performance
D.	entropy
Answer» B. collinerity

10.	Data used to build a data mining model.
A.	training data
B.	validation data
C.	test data
D.	hidden data
Answer» A. training data

11.	The problem of finding hidden structure in unlabeled data is called…
A.	supervised learning
B.	unsupervised learning
C.	reinforcement learning
D.	none of the above
Answer» B. unsupervised learning

12.	Of the Following Examples, Which would you address using an supervised learning Algorithm?
A.	given email labeled as spam or not spam, learn a spam filter
B.	given a set of news articles found on the web, group them into set of articles about the same story.
C.	given a database of customer data, automatically discover market segments and group customers into different market segments.
D.	find the patterns in market basket analysis
Answer» A. given email labeled as spam or not spam, learn a spam filter

13.	Dimensionality Reduction Algorithms are one of the possible ways to reduce the computation time required to build a model
A.	true
B.	false
Answer» A. true

14.	You are given reviews of few netflix series marked as positive, negative and neutral. Classifying reviews of a new netflix series is an example of
A.	supervised learning
B.	unsupervised learning
C.	semisupervised learning
D.	reinforcement learning
Answer» A. supervised learning

18.	Following is powerful distance metrics used by Geometric model
A.	euclidean distance
B.	manhattan distance
C.	both a and b??
D.	square distance
Answer» C. both a and b??

15.	Which of the following is a good test dataset characteristic?
A.	large enough to yield meaningful results
B.	is representative of the dataset as a whole
C.	both a and b
D.	none of the above
Answer» C. both a and b

16.	Following are the types of supervised learning
A.	classification
B.	regression
C.	subgroup discovery
D.	all of the above
Answer» D. all of the above

17.	Type of matrix decomposition model is
A.	descriptive model
B.	predictive model
C.	logical model
D.	none of the above
Answer» A. descriptive model

19.	The output of training process in machine learning is
A.	machine learning model
B.	machine learning algorithm
C.	null
D.	accuracy
Answer» A. machine learning model

20.	A feature F1 can take certain value: A, B, C, D, E, & F and represents grade of students from a college. Here feature type is
A.	nominal
B.	ordinal
C.	categorical
D.	boolean
Answer» B. ordinal

21.	PCA is
A.	forward feature selection
B.	backword feature selection
C.	feature extraction
D.	all of the above
Answer» C. feature extraction

23.	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
Answer» A. removing columns which have too many missing values

24.	Supervised learning and unsupervised clustering both require which is correct according to the statement.
A.	output attribute.
B.	hidden attribute.
C.	input attribute.
D.	categorical attribute
Answer» C. input attribute.

25.	What characterize is hyperplance in geometrical model of machine learning?
A.	a plane with 1 dimensional fewer than number of input attributes
B.	a plane with 2 dimensional fewer than number of input attributes
C.	a plane with 1 dimensional more than number of input attributes
D.	a plane with 2 dimensional more than number of input attributes
Answer» B. a plane with 2 dimensional fewer than number of input attributes

26.	Like the probabilistic view, the ________ view allows us to associate a probability of membership with each classification.
A.	exampler
B.	deductive
C.	classical
D.	inductive
Answer» D. inductive

730+ Machine Learning (ML) Solved MCQs

Application of machine learning methods to large databases is called

If machine learning model output involves target variable then that model is called as

In what type of learning labelled training data is used

In following type of feature selection method we start with empty feature set

In PCA the number of input dimensiona are equal to principal components

PCA can be used for projecting and visualizing data in lower dimensions.

Which of the following is the best machine learning method?

What characterize unlabeled examples in machine learning

What does dimensionality reduction reduce?

Data used to build a data mining model.

The problem of finding hidden structure in unlabeled data is called…

Of the Following Examples, Which would you address using an supervised learning Algorithm?

Dimensionality Reduction Algorithms are one of the possible ways to reduce the computation time required to build a model

You are given reviews of few netflix series marked as positive, negative and neutral. Classifying reviews of a new netflix series is an example of

Which of the following is a good test dataset characteristic?

Following are the types of supervised learning

Type of matrix decomposition model is

Following is powerful distance metrics used by Geometric model

The output of training process in machine learning is

A feature F1 can take certain value: A, B, C, D, E, & F and represents grade of students from a college. Here feature type is

PCA is

Dimensionality reduction algorithms are one of the possible ways to reduce the computation time required to build a model.

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

Supervised learning and unsupervised clustering both require which is correct according to the statement.

What characterize is hyperplance in geometrical model of machine learning?

Like the probabilistic view, the ________ view allows us to associate a probability of membership with each classification.

Database query is used to uncover this type of knowledge.

A person trained to interact with a human expert in order to capture their knowledge.

Some telecommunication company wants to segment their customers into distinct groups ,this is an example of

In the example of predicting number of babies based on stork's population ,Number of babies is

Which learning Requires Self Assessment to identify patterns within data?

Select the correct answers for following statements. 1. Filter methods are much faster compared to wrapper methods. 2. Wrapper methods use statistical methods for evaluation of a subset of features while Filter methods use cross validation.

The "curse of dimensionality" referes

In simple term, machine learning is

If machine learning model output doesnot involves target variable then that model is called as

Following are the descriptive models

Different learning methods does not include?

A measurable property or parameter of the data-set is

Feature can be used as a

It is not necessary to have a target variable for applying dimensionality reduction algorithms

Which of the following is a reasonable way to select the number of principal components "k"?

Which of the folllowing is an example of feature extraction?

Prediction is

You are given sesimic data and you want to predict next earthquake , this is an example of

PCA works better if there is 1. A linear structure in the data 2. If the data lies on a curved surface and not on a flat surface 3. If variables are scaled in the same unit

A student Grade is a variable F1 which takes a value from A,B,C and D. Which of the following is True in the following case?

What can be major issue in Leave-One-Out-Cross-Validation(LOOCV)?

Imagine a Newly-Born starts to learn walking. It will try to find a suitable policy to learn walking after repeated falling and getting up.specify what type of machine learning is best suited?

Support Vector Machine is

Select the correct answers for following statements.
1. Filter methods are much faster compared to wrapper methods.
2. Wrapper methods use statistical methods for evaluation of a subset of features while Filter methods use cross validation.

PCA works better if there is
1. A linear structure in the data
2. If the data lies on a curved surface and not on a flat surface
3. If variables are scaled in the same unit

27.	Database query is used to uncover this type of knowledge.
A.	deep
B.	hidden
C.	shallow
D.	multidimensional
Answer» D. multidimensional

28.	A person trained to interact with a human expert in order to capture their knowledge.
A.	knowledge programmer
B.	knowledge developer r
C.	knowledge engineer
D.	knowledge extractor
Answer» D. knowledge extractor

30.	In the example of predicting number of babies based on stork's population ,Number of babies is
A.	outcome
B.	feature
C.	observation
D.	attribute
Answer» A. outcome

32.	Select the correct answers for following statements. 1. Filter methods are much faster compared to wrapper methods. 2. Wrapper methods use statistical methods for evaluation of a subset of features while Filter methods use cross validation.
A.	both are true
B.	1 is true and 2 is false
C.	both are false
D.	1 is false and 2 is true
Answer» B. 1 is true and 2 is false

33.	The "curse of dimensionality" referes
A.	all the problems that arise when working with data in the higher dimensions, that did not exist in the lower dimensions.
B.	all the problems that arise when working with data in the lower dimensions, that did not exist in the higher dimensions.
C.	all the problems that arise when working with data in the lower dimensions, that did not exist in the lower dimensions.
D.	all the problems that arise when working with data in the higher dimensions, that did not exist in the higher dimensions.
Answer» A. all the problems that arise when working with data in the higher dimensions, that did not exist in the lower dimensions.

34.	In simple term, machine learning is
A.	training based on historical data
B.	prediction to answer a query
C.	both a and b??
D.	automization of complex tasks
Answer» C. both a and b??

36.	Following are the descriptive models
A.	clustering
B.	classification
C.	association rule
D.	both a and c
Answer» D. both a and c

37.	Different learning methods does not include?
A.	memorization
B.	analogy
C.	deduction
D.	introduction
Answer» D. introduction

39.	Feature can be used as a
A.	binary split
B.	predictor
C.	both a and b??
D.	none of the above
Answer» C. both a and b??

41.	The most popularly used dimensionality reduction algorithm is Principal Component Analysis (PCA). Which of the following is/are true about PCA? 1. PCA is an unsupervised method2. It searches for the directions that data have the largest variance3. Maximum number of principal components <= number of features4. All principal components are orthogonal to each other
A.	1 & 2
B.	2 & 3
C.	3 & 4
D.	all of the above
Answer» D. all of the above