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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301.	A feature F1 can take certain value: A, B, C, D, E, & F and represents grade of students from a college. Which of the following statement is true in following case?
A.	feature f1 is an example of nominal variable.
B.	feature f1 is an example of ordinal variable.
C.	it doesn't belong to any of the above category.
D.	both of these
Answer» B. feature f1 is an example of ordinal variable.

302.	What would you do in PCA to get the same projection as SVD?
A.	transform data to zero mean
B.	transform data to zero median
C.	not possible
D.	none of these
Answer» A. transform data to zero mean

303.	What is PCA, KPCA and ICA used for?
A.	principal components analysis
B.	kernel based principal component analysis
C.	independent component analysis
D.	all above
Answer» D. all above

304.	Can a model trained for item based similarity also choose from a given set of items?
A.	yes
B.	no
Answer» A. yes

305.	What are common feature selection methods in regression task?
A.	correlation coefficient
B.	greedy algorithms
C.	all above
D.	none of these
Answer» C. all above

306.	The parameter allows specifying the percentage of elements to put into the test/training set
A.	test_size
B.	training_size
C.	all above
D.	none of these
Answer» C. all above

307.	In many classification problems, the target is made up of categorical labels which cannot immediately be processed by any algorithm.
A.	random_state
B.	dataset
C.	test_size
D.	all above
Answer» B. dataset

308.	adopts a dictionary-oriented approach, associating to each category label a progressive integer number.
A.	labelencoder class
B.	labelbinarizer class
C.	dictvectorizer
D.	featurehasher
Answer» A. labelencoder class

309.	If Linear regression model perfectly first i.e., train error is zero, then
A.	test error is also always zero
B.	test error is non zero
C.	couldn't comment on test error
D.	test error is equal to train error
Answer» C. couldn't comment on test error

310.	Which of the following metrics can be used for evaluating regression models? i) R Squared ii) Adjusted R Squared iii) F Statistics iv) RMSE / MSE / MAE
A.	ii and iv
B.	i and ii
C.	ii, iii and iv
D.	i, ii, iii and iv
Answer» D. i, ii, iii and iv

311.	In a simple linear regression model (One independent variable), If we change the input variable by 1 unit. How much output variable will change?
A.	by 1
B.	no change
C.	by intercept
D.	by its slope
Answer» D. by its slope

312.	Function used for linear regression in R is
A.	lm(formula, data)
B.	lr(formula, data)
C.	lrm(formula, data)
D.	regression.linear(formula, data)
Answer» A. lm(formula, data)

313.	In syntax of linear model lm(formula,data,..), data refers to
A.	matrix
B.	vector
C.	array
D.	list
Answer» B. vector

314.	In the mathematical Equation of Linear Regression Y = β1 + β2X + ϵ, (β1, β2) refers to
A.	(x-intercept, slope)
B.	(slope, x-intercept)
C.	(y-intercept, slope)
D.	(slope, y-intercept)
Answer» C. (y-intercept, slope)

316.	It is possible to design a Linear regression algorithm using a neural network?
A.	true
B.	false
Answer» A. true

315.	Linear Regression is a supervised machine learning algorithm.
A.	true
B.	false
Answer» A. true

317.	Overfitting is more likely when you have huge amount of data to train?
A.	true
B.	false
Answer» B. false

318.	Which of the following statement is true about outliers in Linear regression?
A.	linear regression is sensitive to outliers
B.	linear regression is not sensitive to outliers
C.	can't say
D.	none of these
Answer» A. linear regression is sensitive to outliers

319.	Suppose you plotted a scatter plot between the residuals and predicted values in linear regression and you found that there is a relationship between them. Which of the following conclusion do you make about this situation?
A.	since the there is a relationship means our model is not good
B.	since the there is a relationship means our model is good
C.	can't say
D.	none of these
Answer» A. since the there is a relationship means our model is not good

320.	Naive Bayes classifiers are a collection ------------------of algorithms
A.	classification
B.	clustering
C.	regression
D.	all
Answer» A. classification

321.	Naive Bayes classifiers is Learning
A.	supervised
B.	unsupervised
C.	both
D.	none
Answer» A. supervised

322.	Features being classified is independent of each other in Nave Bayes Classifier
A.	false
B.	true
Answer» B. true

323.	Features being classified is of each other in Nave Bayes Classifier
A.	independent
B.	dependent
C.	partial dependent
D.	none
Answer» A. independent

324.	Bayes Theorem is given by where 1. P(H) is the probability of hypothesis H being true. 2. P(E) is the probability of the evidence(regardless of the hypothesis). 3. P(E\|H) is the probability of the evidence given that hypothesis is true. 4. P(H\|E) is the probability of the hypothesis given that the evidence is there.
A.	true
B.	false
Answer» A. true

325.	In given image, P(H\|E) is probability.
A.	posterior
B.	prior
Answer» A. posterior

730+ Machine Learning (ML) Solved MCQs

A feature F1 can take certain value: A, B, C, D, E, & F and represents grade of students from a college. Which of the following statement is true in following case?

What would you do in PCA to get the same projection as SVD?

What is PCA, KPCA and ICA used for?

Can a model trained for item based similarity also choose from a given set of items?

What are common feature selection methods in regression task?

The parameter allows specifying the percentage of elements to put into the test/training set

In many classification problems, the target is made up of categorical labels which cannot immediately be processed by any algorithm.

adopts a dictionary-oriented approach, associating to each category label a progressive integer number.

If Linear regression model perfectly first i.e., train error is zero, then

Which of the following metrics can be used for evaluating regression models? i) R Squared ii) Adjusted R Squared iii) F Statistics iv) RMSE / MSE / MAE

In a simple linear regression model (One independent variable), If we change the input variable by 1 unit. How much output variable will change?

Function used for linear regression in R is

In syntax of linear model lm(formula,data,..), data refers to

In the mathematical Equation of Linear Regression Y = β1 + β2X + ϵ, (β1, β2) refers to

Linear Regression is a supervised machine learning algorithm.

It is possible to design a Linear regression algorithm using a neural network?

Overfitting is more likely when you have huge amount of data to train?

Which of the following statement is true about outliers in Linear regression?

Suppose you plotted a scatter plot between the residuals and predicted values in linear regression and you found that there is a relationship between them. Which of the following conclusion do you make about this situation?

Naive Bayes classifiers are a collection ------------------of algorithms

Naive Bayes classifiers is Learning

Features being classified is independent of each other in Nave Bayes Classifier

Features being classified is of each other in Nave Bayes Classifier

In given image, P(H|E) is probability.

In given image, P(H) is probability.

Conditional probability is a measure of the probability of an event given that another

Bayes theorem describes the probability of an event, based on prior knowledge of conditions that might be related to the event.

Bernoulli Nave Bayes Classifier is distribution

Multinomial Nave Bayes Classifier is distribution

Gaussian Nave Bayes Classifier is distribution

Binarize parameter in BernoulliNB scikit sets threshold for binarizing of sample features.

Gaussian distribution when plotted, gives a bell shaped curve which is symmetric about the of the feature values.

SVMs directly give us the posterior probabilities P(y = 1jx) and P(y = ??1jx)

Any linear combination of the components of a multivariate Gaussian is a univariate Gaussian.

Solving a non linear separation problem with a hard margin Kernelized SVM (Gaussian RBF Kernel) might lead to overfitting

SVM is a algorithm

SVM is a learning

The linearSVMclassifier works by drawing a straight line between two classes

Which of the following function provides unsupervised prediction ?

Which of the following is characteristic of best machine learning method ?

What are the different Algorithm techniques in Machine Learning?

What is the standard approach to supervised learning?

Which of the following is not Machine Learning?

What is Model Selection in Machine Learning?

Which are two techniques of Machine Learning ?

Even if there are no actual supervisors learning is also based on feedback provided by the environment

What does learning exactly mean?

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 .

Which of the following metrics can be used for evaluating regression models?
i) R Squared
ii) Adjusted R Squared
iii) F Statistics
iv) RMSE / MSE / MAE

326.	In given image, P(H) is probability.
A.	posterior
B.	prior
Answer» B. prior

327.	Conditional probability is a measure of the probability of an event given that another
A.	true
B.	false
Answer» A. true

328.	Bayes theorem describes the probability of an event, based on prior knowledge of conditions that might be related to the event.
A.	true
B.	false
Answer» A. true

329.	Bernoulli Nave Bayes Classifier is distribution
A.	continuous
B.	discrete
C.	binary
Answer» C. binary

330.	Multinomial Nave Bayes Classifier is distribution
A.	continuous
B.	discrete
C.	binary
Answer» B. discrete

331.	Gaussian Nave Bayes Classifier is distribution
A.	continuous
B.	discrete
C.	binary
Answer» A. continuous

332.	Binarize parameter in BernoulliNB scikit sets threshold for binarizing of sample features.
A.	true
B.	false
Answer» A. true

333.	Gaussian distribution when plotted, gives a bell shaped curve which is symmetric about the of the feature values.
A.	mean
B.	variance
C.	discrete
D.	random
Answer» A. mean

334.	SVMs directly give us the posterior probabilities P(y = 1jx) and P(y = ??1jx)
A.	true
B.	false
Answer» B. false

335.	Any linear combination of the components of a multivariate Gaussian is a univariate Gaussian.
A.	true
B.	false
Answer» A. true