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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551.	In reinforcement learning, this feedback is usually called as .
A.	overfitting
B.	overlearning
C.	reward
D.	none of above
Answer» C. reward

552.	In the last decade, many researchers started training bigger and bigger models, built with several different layers that's why this approach is called .
A.	deep learning
B.	machine learning
C.	reinforcement learning
D.	unsupervised learning
Answer» A. deep learning

553.	When it is necessary to allow the model to develop a generalization ability and avoid a common problem called .
A.	overfitting
B.	overlearning
C.	classification
D.	regression
Answer» A. overfitting

554.	Techniques involve the usage of both labeled and unlabeled data is called .
A.	supervised
B.	semi- supervised
C.	unsupervised
D.	none of the above
Answer» B. semi- supervised

555.	Reinforcement learning is particularly efficient when .
A.	the environment is not completely deterministic
B.	it\s often very dynamic
C.	it\s impossible to have a precise error measure
D.	all above
Answer» D. all above

556.	During the last few years, many algorithms have been applied to deep neural networks to learn the best policy for playing Atari video games and to teach an agent how to associate the right action with an input representing the state.
A.	logical
B.	classical
C.	classification
D.	none of above
Answer» D. none of above

557.	if there is only a discrete number of possible outcomes (called categories), the process becomes a .
A.	regression
B.	classification.
C.	modelfree
D.	categories
Answer» B. classification.

558.	Let’s say, you are working with categorical feature(s) and you have not looked at the distribution of the categorical variable in the test data. You want to apply one hot encoding (OHE) on the categorical feature(s). What challenges you may face if you have applied OHE on a categorical variable of train dataset?
A.	all categories of categorical variable are not present in the test dataset.
B.	frequency distribution of categories is different in train as compared to the test dataset.
C.	train and test always have same distribution.
D.	both a and b
Answer» D. both a and b

559.	scikit-learn also provides functions for creating dummy datasets from scratch:
A.	make_classifica tion()
B.	make_regressio n()
C.	make_blobs()
D.	all above
Answer» D. all above

560.	which can accept a NumPy RandomState generator or an integer seed.
A.	make_blobs
B.	random_state
C.	test_size
D.	training_size
Answer» B. random_state

561.	In many classification problems, the target dataset is made up of categorical labels which cannot immediately be processed by any algorithm. An encoding is needed and scikit-learn offers at least valid options
A.	1
B.	2
C.	3
D.	4
Answer» B. 2

562.	It's possible to specify if the scaling process must include both mean and standard deviation using the parameters .
A.	with_mean=tru e/false
B.	with_std=true/ false
C.	both a & b
D.	none of the mentioned
Answer» C. both a & b

563.	Which of the following selects the best K high-score features.
A.	selectpercentil e
B.	featurehasher
C.	selectkbest
D.	all above
Answer» C. selectkbest

564.	We have been given a dataset with n records in which we have input attribute as x and output attribute as y. Suppose we use a linear regression method to model this data. To test our linear regressor, we split the data in training set and test set randomly. Now we increase the training set size gradually. As the training set size increases, what do you expect will happen with the mean training error?
A.	increase
B.	decrease
C.	remain constant
D.	can’t say
Answer» D. can’t say

565.	We have been given a dataset with n records in which we have input attribute as x and output attribute as y. Suppose we use a linear regression method to model this data. To test our linear regressor, we split the data in training set and test set randomly. What do you expect will happen with bias and variance as you increase the size of training data?
A.	bias increases and variance increases
B.	bias decreases and variance increases
C.	bias decreases and variance decreases
D.	bias increases and variance decreases
Answer» D. bias increases and variance decreases

566.	Problem: Players will play if weather is sunny. Is this statement is correct?
A.	true
B.	false
Answer» A. true

567.	Multinomial Naïve Bayes Classifier is _ distribution
A.	continuous
B.	discrete
C.	binary
Answer» B. discrete

568.	The minimum time complexity for training an SVM is O(n2). According to this fact, what sizes of datasets are not best suited for SVM’s?
A.	large datasets
B.	small datasets
C.	medium sized datasets
D.	size does not matter
Answer» A. large datasets

569.	We usually use feature normalization before using the Gaussian kernel in SVM. What is true about feature normalization? 1. We do feature normalization so that new feature will dominate other 2. Some times, feature normalization is not feasible in case of categorical variables3. Feature normalization always helps when we use Gaussian kernel in SVM
A.	1
B.	1 and 2
C.	1 and 3
D.	2 and 3
Answer» B. 1 and 2

570.	Which of the following is not supervised learning?
A.	pca
B.	decision tree
C.	naive bayesian
D.	linerar regression
Answer» A. pca

571.	Gaussian Naïve Bayes Classifier is _ distribution
A.	continuous
B.	discrete
C.	binary
Answer» A. continuous

572.	If I am using all features of my dataset and I achieve 100% accuracy on my training set, but ~70% on validation set, what should I look out for?
A.	underfitting
B.	nothing, the model is perfect
C.	overfitting
Answer» C. overfitting

573.	The cost parameter in the SVM means:
A.	the number of cross- validations to be made
B.	the kernel to be used
C.	the tradeoff between misclassificati on and simplicity of the model
D.	none of the above
Answer» C. the tradeoff between misclassificati on and simplicity of the model

574.	We usually use feature normalization before using the Gaussian k
A.	e 1
B.	1 and 2
C.	1 and 3
D.	2 and 3
Answer» B. 1 and 2

575.	The effectiveness of an SVM depends upon:
A.	selection of kernel
B.	kernel parameters
C.	soft margin parameter c
D.	all of the above
Answer» D. all of the above

730+ Machine Learning (ML) Solved MCQs

In reinforcement learning, this feedback is usually called as .

In the last decade, many researchers started training bigger and bigger models, built with several different layers that's why this approach is called .

When it is necessary to allow the model to develop a generalization ability and avoid a common problem called .

Techniques involve the usage of both labeled and unlabeled data is called .

Reinforcement learning is particularly efficient when .

During the last few years, many algorithms have been applied to deep neural networks to learn the best policy for playing Atari video games and to teach an agent how to associate the right action with an input representing the state.

if there is only a discrete number of possible outcomes (called categories), the process becomes a .

scikit-learn also provides functions for creating dummy datasets from scratch:

which can accept a NumPy RandomState generator or an integer seed.

In many classification problems, the target dataset is made up of categorical labels which cannot immediately be processed by any algorithm. An encoding is needed and scikit-learn offers at least valid options

It's possible to specify if the scaling process must include both mean and standard deviation using the parameters .

Which of the following selects the best K high-score features.

Problem: Players will play if weather is sunny. Is this statement is correct?

Multinomial Naïve Bayes Classifier is _ distribution

The minimum time complexity for training an SVM is O(n2). According to this fact, what sizes of datasets are not best suited for SVM’s?

Which of the following is not supervised learning?

Gaussian Naïve Bayes Classifier is _ distribution

If I am using all features of my dataset and I achieve 100% accuracy on my training set, but ~70% on validation set, what should I look out for?

The cost parameter in the SVM means:

We usually use feature normalization before using the Gaussian k

The effectiveness of an SVM depends upon:

The process of forming general concept definitions from examples of concepts to be learned.

Computers are best at learning

Data used to build a data mining model.

Supervised learning and unsupervised clustering both require at least one

Supervised learning differs from unsupervised clustering in that supervised learning requires

A regression model in which more than one independent variable is used to predict the dependent variable is called

A term used to describe the case when the independent variables in a multiple regression model are correlated is

A multiple regression model has the form: y = 2 + 3x1 + 4x2. As x1 increases by 1 unit (holding x2 constant), y will

A multiple regression model has

A measure of goodness of fit for the estimated regression equation is the

The adjusted multiple coefficient of determination accounts for

The multiple coefficient of determination is computed by

For a multiple regression model, SST = 200 and SSE = 50. The multiple coefficient of determination is

A nearest neighbor approach is best used

Another name for an output attribute.

Classification problems are distinguished from estimation problems in that

Which statement is true about prediction problems?

Which of the following is a common use of unsupervised clustering?

The average positive difference between computed and desired outcome values.

Selecting data so as to assure that each class is properly represented in both the training and test set.

The standard error is defined as the square root of this computation.

Data used to optimize the parameter settings of a supervised learner model.

Bootstrapping allows us to

The correlation coefficient for two real-valued attributes is –0.85. What does this value tell you?

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

576.	The process of forming general concept definitions from examples of concepts to be learned.
A.	deduction
B.	abduction
C.	induction
D.	conjunction
Answer» C. induction

577.	Computers are best at learning
A.	facts.
B.	concepts.
C.	procedures.
D.	principles.
Answer» A. facts.

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

579.	Supervised learning and unsupervised clustering both require at least one
A.	hidden attribute.
B.	output attribute.
C.	input attribute.
D.	categorical attribute.
Answer» A. hidden attribute.

580.	Supervised learning differs from unsupervised clustering in that supervised learning requires
A.	at least one input attribute.
B.	input attributes to be categorical.
C.	at least one output attribute.
D.	output attributes to be categorical.
Answer» B. input attributes to be categorical.

581.	A regression model in which more than one independent variable is used to predict the dependent variable is called
A.	a simple linear regression model
B.	a multiple regression models
C.	an independent model
D.	none of the above
Answer» C. an independent model

582.	A term used to describe the case when the independent variables in a multiple regression model are correlated is
A.	regression
B.	correlation
C.	multicollinearity
D.	none of the above
Answer» C. multicollinearity

583.	A multiple regression model has the form: y = 2 + 3x1 + 4x2. As x1 increases by 1 unit (holding x2 constant), y will
A.	increase by 3 units
B.	decrease by 3 units
C.	increase by 4 units
D.	decrease by 4 units
Answer» A. increase by 3 units

584.	A multiple regression model has
A.	only one independent variable
B.	more than one dependent variable
C.	more than one independent variable
D.	none of the above
Answer» B. more than one dependent variable

585.	A measure of goodness of fit for the estimated regression equation is the
A.	multiple coefficient of determination
B.	mean square due to error
C.	mean square due to regression
D.	none of the above
Answer» C. mean square due to regression