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.

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501.	Which of the following sentence is FALSE regarding regression?
A.	it relates inputs to outputs.
B.	it is used for prediction.
C.	it may be used for interpretation.
D.	it discovers causal relationships.
Answer» D. it discovers causal relationships.

502.	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

503.	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

504.	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

505.	is the most drastic one and should be considered only when the dataset is quite large, the number of missing features is high, and any prediction could be risky.
A.	removing the whole line
B.	creating sub- model to predict those features
C.	using an automatic strategy to input them according to the other known values
D.	all above
Answer» A. removing the whole line

506.	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

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

508.	Suppose you have fitted a complex regression model on a dataset. Now, you are using Ridge regression with tuning parameter lambda to reduce its complexity. Choose the option(s) below which describes relationship of bias and variance with lambda.
A.	in case of very large lambda; bias is low, variance is low
B.	in case of very large lambda; bias is low, variance is high
C.	in case of very large lambda; bias is high, variance is low
D.	in case of very large lambda; bias is high, variance is high
Answer» C. in case of very large lambda; bias is high, variance is low

509.	What is/are true about ridge regression? 1. When lambda is 0, model works like linear regression model 2. When lambda is 0, model doesn’t work like linear regression model 3. When lambda goes to infinity, we get very, very small coefficients approaching 0 4. When lambda goes to infinity, we get very, very large coefficients approaching infinity
A.	1 and 3
B.	1 and 4
C.	2 and 3
D.	2 and 4
Answer» A. 1 and 3

510.	Which of the following method(s) does not have closed form solution for its coefficients?
A.	ridge regression
B.	lasso
C.	both ridge and lasso
D.	none of both
Answer» B. lasso

511.	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)

512.	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)

513.	Suppose that we have N independent variables (X1,X2… Xn) and dependent variable is Y. Now Imagine that you are applying linear regression by fitting the best fit line using least square error on this data. You found that correlation coefficient for one of it’s variable(Say X1) with Y is -0.95.Which of the following is true for X1?
A.	relation between the x1 and y is weak
B.	relation between the x1 and y is strong
C.	relation between the x1 and y is neutral
D.	correlation can’t judge the relationship
Answer» B. relation between the x1 and y is strong

514.	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

516.	Suppose, you got a situation where you find that your linear regression model is under fitting the data. In such situation which of the following options would you consider?1. I will add more variables2. I will start introducing polynomial degree variables3. I will remove some variables
A.	1 and 2
B.	2 and 3
C.	1 and 3
D.	1, 2 and 3
Answer» A. 1 and 2

515.	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

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

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

519.	For the given weather data, Calculate probability of not playing
A.	0.4
B.	0.64
C.	0.36
D.	0.5
Answer» C. 0.36

520.	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

521.	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

522.	What do you mean by generalization error in terms of the SVM?
A.	how far the hyperplane is from the support vectors
B.	how accurately the svm can predict outcomes for unseen data
C.	the threshold amount of error in an svm
Answer» B. how accurately the svm can predict outcomes for unseen data

523.	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

524.	Support vectors are the data points that lie closest to the decision surface.
A.	true
B.	false
Answer» A. true

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

730+ Machine Learning (ML) Solved MCQs

Which of the following sentence is FALSE regarding regression?

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

is the most drastic one and should be considered only when the dataset is quite large, the number of missing features is high, and any prediction could be risky.

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.

Suppose you have fitted a complex regression model on a dataset. Now, you are using Ridge regression with tuning parameter lambda to reduce its complexity. Choose the option(s) below which describes relationship of bias and variance with lambda.

Which of the following method(s) does not have closed form solution for its coefficients?

Function used for linear regression in R is

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

Suppose, you got a situation where you find that your linear regression model is under fitting the data. In such situation which of the following options would you consider?1. I will add more variables2. I will start introducing polynomial degree variables3. I will remove some variables

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

Multinomial Naïve Bayes Classifier is _ distribution

For the given weather data, Calculate probability of not playing

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?

The effectiveness of an SVM depends upon:

What do you mean by generalization error in terms of the SVM?

Support vectors are the data points that lie closest to the decision surface.

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?

What is the purpose of performing cross- validation?

Linear SVMs have no hyperparameters that need to be set by cross-validation

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

100 people are at party. Given data gives information about how many wear pink or not, and if a man or not. Imagine a pink wearing guest leaves, what is the probability of being a man

Problem: Players will play if weather is sunny. Is t

For the given weather data, Calculate probability

For the given weather data, Calculate probability

For the given weather data, what is the probabilit

100 people are at party. Given data gives informa

100 people are at party. Given data gives informa

What do you mean by generalization error in terms of the SVM?

The effectiveness of an SVM depends upon:

Support vectors are the data points that lie closest to the decision

The SVM’s are less effective when:

Suppose you are using RBF kernel in SVM with high Gamma valu

The cost parameter in the SVM means:

If I am using all features of my dataset and I achieve 100% accura

Which of the following are real world applications of the SVM?

Suppose you have trained an SVM with linear decision boundary after training SVM, you correctly infer that your SVM model is under fitting.Which of the following option would you more likely to consider iterating SVM next time?

We usually use feature normalization before using the Gaussian k

Linear SVMs have no hyperparameters that need to be set by cross-valid

In a real problem, you should check to see if the SVM is separable and th

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

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?

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

527.	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

528.	What is the purpose of performing cross- validation?
A.	to assess the predictive performance of the models
B.	to judge how the trained model performs outside the sample ontest data
C.	both a and b
Answer» C. both a and b

529.	Suppose you are using a Linear SVM classifier with 2 class classification problem. Now you have been given the following data in which some points are circled red that are representing support vectors.If you remove the following any one red points from the data. Does the decision boundary will change?
A.	yes
B.	no
Answer» A. yes

530.	Linear SVMs have no hyperparameters that need to be set by cross-validation
A.	true
B.	false
Answer» B. false

531.	For the given weather data, what is the probability that players will play if weather is sunny
A.	0.5
B.	0.26
C.	0.73
D.	0.6
Answer» D. 0.6

532.	100 people are at party. Given data gives information about how many wear pink or not, and if a man or not. Imagine a pink wearing guest leaves, what is the probability of being a man
A.	0.4
B.	0.2
C.	0.6
D.	0.45
Answer» B. 0.2

533.	Problem: Players will play if weather is sunny. Is t
A.	true
B.	false
Answer» A. true

534.	For the given weather data, Calculate probability
A.	0.4
B.	0.64
C.	0.29
D.	0.75
Answer» B. 0.64

535.	For the given weather data, Calculate probability
A.	0.4
B.	0.64
C.	0.36
D.	0.5
Answer» C. 0.36