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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451.	scikit-learn also provides functions for creating dummy datasets from scratch:
A.	make_classification()
B.	make_regression()
C.	make_blobs()
D.	all above
Answer» D. all above

452.	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

453.	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

454.	In which of the following each categorical label is first turned into a positive integer and then transformed into a vector where only one feature is 1 while all the others are 0.
A.	labelencoder class
B.	dictvectorizer
C.	labelbinarizer class
D.	featurehasher
Answer» C. labelbinarizer class

455.	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

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

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

458.	How does number of observations influence overfitting? Choose the correct answer(s).Note: Rest all parameters are same1. In case of fewer observations, it is easy to overfit the data.2. In case of fewer observations, it is hard to overfit the data.3. In case of more observations, it is easy to overfit the data.4. In case of more observations, it is hard to overfit the data.
A.	1 and 4
B.	2 and 3
C.	1 and 3
D.	none of theses
Answer» A. 1 and 4

459.	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

460.	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

461.	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

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

463.	In the mathematical Equation of Linear Regression Y?=??1 + ?2X + ?, (?1, ?2) refers to
A.	(x-intercept, slope)
B.	(slope, x-intercept)
C.	(y-intercept, slope)
D.	(slope, y-intercept)
Answer» C. (y-intercept, slope)

464.	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 its 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

465.	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

466.	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

467.	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 variables 2. I will start introducing polynomial degree variables 3. 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

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

469.	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

470.	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?
A.	you want to increase your data points
B.	you want to decrease your data points
C.	you will try to calculate more variables
D.	you will try to reduce the features
Answer» C. you will try to calculate more variables

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

472.	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

473.	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 variables 3. 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

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

475.	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

730+ Machine Learning (ML) Solved MCQs

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

In which of the following each categorical label is first turned into a positive integer and then transformed into a vector where only one feature is 1 while all the others are 0.

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

In the mathematical Equation of Linear Regression Y?=??1 + ?2X + ?, (?1, ?2) 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 variables 2. I will start introducing polynomial degree variables 3. I will remove some variables

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

For the given weather data, Calculate probability of not playing

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?

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

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.

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

Linear SVMs have no hyperparameters

What are the different Algorithm techniques in Machine Learning?

can be adopted when it's necessary to categorize a large amount of data with a few complete examples or when there's the need to

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 .

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 .

there's a growing interest in pattern recognition and associative memories whose structure and functioning are similar to what happens in the neocortex. Such an

showed better performance than other approaches, even without a context-based model

Which of the following sentence is correct?

What is ‘Overfitting’ in Machine learning?

What is ‘Test set’?

what is the function of ‘Supervised Learning’?

Commons unsupervised applications include

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.

Common deep learning applications include

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

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 variables
2. I will start introducing polynomial degree variables
3. I will remove some variables

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.

476.	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
C.	both a and b
Answer» C. both a and b

477.	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

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

479.	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

480.	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

481.	Linear SVMs have no hyperparameters
A.	true
B.	false
Answer» B. false

482.	What are the different Algorithm techniques in Machine Learning?
A.	supervised learning and semi-
B.	unsupervised learning and transduction
C.	both a & b
D.	none of the mentioned
Answer» C. both a & b

483.	can be adopted when it's necessary to categorize a large amount of data with a few complete examples or when there's the need to
A.	supervised
B.	semi- supervised
C.	reinforcement
D.	clusters
Answer» B. semi- supervised

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

485.	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