Suppose you are building a SVM model on data X. The data X can be error prone which means that you should not trust any specific data point too much. Now think that you want to build a SVM model which has quadratic kernel function of polynomial degree 2 that uses Slack variable C as one of itâ€™s hyper parameter.What would happen when you use very small C (C~0)?

Data will be correctly classified

Common deep learning applications / problems can also be solved using____

Real-time visual object identification

what is the function of â€˜Unsupervised Learningâ€™?

Find clusters of the data and find low-dimensional representations of the data

Find interesting directions in data and find novel observations/ database cleaning

Interesting coordinates and correlations

Letâ€™s say, a â€œLinear regressionâ€ model perfectly fits the training data (train error is zero). Now, Which of the following statement is true?

You will always have test error zero

You can not have test error zero

In a linear regression problem, we are using â€œR-squaredâ€ to measure goodness-of-fit. We add a feature in linear regression model and retrain the same model.Which of the following option is true?

A. If R Squared increases, this variable is significant.

B. If R Squared decreases, this variable is not significant.

C. Individually R squared cannot tell about variable importance. We canâ€™t say anything about it right now.

Which of the following is true about â€œRidgeâ€ or â€œLassoâ€ regression methods in case of feature selection?

C. Both use subset selection of features

A. Ridge regression uses subset selection of features

B. Lasso regression uses subset selection of features

C. Both use subset selection of features

Suppose you are building a SVM model on data X. The data X can be error prone which means that you should not trust any specific data point too much. Now think that you want to build a SVM model which has quadratic kernel function of polynomial degree 2 that uses Slack variable C as one of itâ€™s hyper parameter.What would happen when you use very large value of C(C->infinity)?

We can not classify data correctly for given setting of hyper parameter C

We can still classify data correctly for given setting of hyper parameter C

We can not classify data correctly for given setting of hyper parameter C

What is the purpose of performing cross-validation?

To assess the predictive performance of the models

To judge how the trained model performs outside the sample on test data

Suppose we fit â€œLasso Regressionâ€ to a data set, which has 100 features (X1,X2â€¦X100).Â Now, we rescale one of these feature by multiplying with 10 (say that feature is X1),Â and then refit Lasso regression with the same regularization parameter.Now, which of the following option will be correct?

It is more likely for X1 to be excluded from the model

It is more likely for X1 to be included in the model

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

Test error is equal to Train error

Couldnâ€™t comment on Test error

Test error is equal to Train error

Which of the following option is true regarding â€œRegressionâ€ and â€œCorrelationâ€ ?Note: y is dependent variable and x is independent variable.

The relationship is symmetric between x and y in both.

The relationship is not symmetric between x and y in both.

The relationship is not symmetric between x and y in case of correlation but in case of regression it is symmetric.

The relationship is symmetric between x and y in case of correlation but in case of regression it is not symmetric.

______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.

Creating sub-model to predict those features

Using an automatic strategy to input them according to the other known values

Â Function used for linear regression in R is __________

regression.linear(formula, data)

Lets say, a Linear regression model perfectly fits the training data (train error is zero). Now, Which of the following statement is true?

you will always have test error zero

you can not have test error zero

which of the following step / assumption in regression modeling impacts the trade- off between under-fitting and over-fitting the most.

whether we learn the weights by matrix inversion or gradient descent

Suppose you are building a SVM model on data X. The data X can be error prone which means that you should not trust any specific data point too much. Now think that you want to build a SVM model which has quadratic kernel function of polynomial degree 2 that uses Slack variable C as one of its hyper parameter.What would happen when you use very small C (C~0)?

data will be correctly classified

How do you handle missing or corrupted data in a dataset?

a. drop missing rows or columns

b. replace missing values with mean/median/mode

c. assign a unique category to missing values

The SVMs are less effective when:

the data is clean and ready to use

the data is noisy and contains overlapping points

Common deep learning applications / problems can also be solved using

real-time visual object identification

607 + Mcqs in Machine Learning in Computer Science Engineering (CSE) Page 2 McqOptions

51.	Suppose you are building a SVM model on data X. The data X can be error prone which means that you should not trust any specific data point too much. Now think that you want to build a SVM model which has quadratic kernel function of polynomial degree 2 that uses Slack variable C as one of itâ€™s hyper parameter.What would happen when you use very small C (C~0)?
A.	Misclassification would happen
B.	Data will be correctly classified
C.	Canâ€™t say
D.	None of these
Answer» B. Data will be correctly classified

Discussion

52.	If there is only a discrete number of possible outcomes called _____.
A.	Modelfree
B.	Categories
C.	Prediction
D.	None of above
Answer» C. Prediction

Discussion

53.	Some people are using the term ___ instead of prediction only to avoid the weird idea that machine learning is a sort of modern magic.
A.	Inference
B.	Interference
C.	Accuracy
D.	None of above
Answer» B. Interference

Discussion

54.	The term _____ can be freely used, but with the same meaning adopted in physics or system theory.
A.	Accuracy
B.	Cluster
C.	Regression
D.	Prediction
Answer» E.

Discussion

55.	Common deep learning applications / problems can also be solved using____
A.	Real-time visual object identification
B.	Classic approaches
C.	Automatic labeling
D.	Bio-inspired adaptive systems
Answer» C. Automatic labeling

Discussion

56.	what is the function of â€˜Unsupervised Learningâ€™?
A.	Find clusters of the data and find low-dimensional representations of the data
B.	Find interesting directions in data and find novel observations/ database cleaning
C.	Interesting coordinates and correlations
D.	All
Answer» E.

Discussion

57.	Letâ€™s say, a â€œLinear regressionâ€ model perfectly fits the training data (train error is zero). Now, Which of the following statement is true?
A.	You will always have test error zero
B.	You can not have test error zero
C.	None of the above
Answer» D.

Discussion

58.	In a linear regression problem, we are using â€œR-squaredâ€ to measure goodness-of-fit. We add a feature in linear regression model and retrain the same model.Which of the following option is true?
A.	A. If R Squared increases, this variable is significant.
B.	B. If R Squared decreases, this variable is not significant.
C.	C. Individually R squared cannot tell about variable importance. We canâ€™t say anything about it right now.
D.	D. None of these.
Answer» D. D. None of these.

Discussion

59.	Which of the following is true about â€œRidgeâ€ or â€œLassoâ€ regression methods in case of feature selection?
A.	A. Ridge regression uses subset selection of features
B.	B. Lasso regression uses subset selection of features
C.	C. Both use subset selection of features
D.	D. None of above
Answer» C. C. Both use subset selection of features

Discussion

60.	Which of the following statement(s) can be true post adding a variable in a linear regression model?1. R-Squared and Adjusted R-squared both increase2. R-Squared increases and Adjusted R-squared decreases3. R-Squared decreases and Adjusted R-squared decreases4. R-Squared decreases and Adjusted R-squared increases
A.	A. 1 and 2
B.	B. 1 and 3
C.	C. 2 and 4
D.	D. None of the above
Answer» B. B. 1 and 3

Discussion

61.	Suppose you are building a SVM model on data X. The data X can be error prone which means that you should not trust any specific data point too much. Now think that you want to build a SVM model which has quadratic kernel function of polynomial degree 2 that uses Slack variable C as one of itâ€™s hyper parameter.What would happen when you use very large value of C(C->infinity)?
A.	We can still classify data correctly for given setting of hyper parameter C
B.	We can not classify data correctly for given setting of hyper parameter C
C.	Canâ€™t Say
D.	None of these
Answer» B. We can not classify data correctly for given setting of hyper parameter C

Discussion

62.	SVM can solveÂ linearÂ and non-linearÂ problems
A.	true
B.	false
Answer» B. false

Discussion

63.	The objective of the support vector machine algorithm is to find a hyperplane in an N-dimensional space(N â€” the number of features) that distinctly classifies the data points.
A.	true
B.	false
Answer» B. false

Discussion

64.	Hyperplanes are _____________boundaries that help classify the data points.
A.	usual
B.	decision
C.	parallel
Answer» C. parallel

Discussion

65.	The _____of the hyperplane depends upon the number of features.
A.	dimension
B.	classification
C.	reduction
Answer» B. classification

Discussion

66.	SVMÂ algorithmsÂ useÂ a set of mathematical functions that are defined as theÂ kernel.
A.	true
B.	false
Answer» B. false

Discussion

67.	Which of the following isÂ notÂ supervised learning?
A.	Â Â PCA
B.	Â Â Decision Tree
C.	Â Â Naive Bayesian
D.	Linerar regression
Answer» B. Â Â Decision Tree

Discussion

68.	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 on test data
C.	Both A and B
Answer» D.

Discussion

69.	______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 impose some constraints to a clustering algorithm.
A.	Supervised
B.	Semi-supervised
C.	Reinforcement
D.	Clusters
Answer» C. Reinforcement

Discussion

70.	In reinforcement learning, this feedback is usually called as___.
A.	Overfitting
B.	Overlearning
C.	Reward
D.	None of above
Answer» D. None of above

Discussion

71.	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» B. Machine learning

Discussion

72.	Â If two variables are correlated, is it necessary that they have a linear relationship?
A.	Yes
B.	No
Answer» C.

Discussion

73.	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 approach also allows simpler algorithms called _____
A.	Regression
B.	Accuracy
C.	Modelfree
D.	Scalable
Answer» D. Scalable

Discussion

74.	______ showed better performance than other approaches, even without a context-based model
A.	Machine learning
B.	Deep learning
C.	Reinforcement learning
D.	Supervised learning
Answer» C. Reinforcement learning

Discussion

75.	Suppose we fit â€œLasso Regressionâ€ to a data set, which has 100 features (X1,X2â€¦X100).Â Now, we rescale one of these feature by multiplying with 10 (say that feature is X1),Â and then refit Lasso regression with the same regularization parameter.Now, which of the following option will be correct?
A.	It is more likely for X1 to be excluded from the model
B.	It is more likely for X1 to be included in the model
C.	Canâ€™t say
D.	None of these
Answer» C. Canâ€™t say

Discussion

76.	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.	Couldnâ€™t comment on Test error
D.	Test error is equal to Train error
Answer» D. Test error is equal to Train error

Discussion

77.	In syntax of linear model lm(formula,data,..), data refers to ______
A.	Matrix
B.	Vector
C.	Array
D.	List
Answer» C. Array

Discussion

78.	Â Â Â Â Â Â Â Â Â Â Â allows exploiting the natural sparsity of data while extracting principal components.
A.	sparsepca
B.	kernelpca
C.	svd
D.	init parameter
Answer» B. kernelpca

Discussion

79.	We can also compute the coefficient of linear regression with the help of an analytical method called â€œNormal Equationâ€. Which of the following is/are true about â€œNormal Equationâ€?1. We donâ€™t have to choose the learning rate2. It becomes slow when number of features is very large3. No need to iterate
A.	1 and 2
B.	1 and 3.
C.	2 and 3.
D.	1,2 and 3.
Answer» E.

Discussion

80.	Which of the following option is true regarding â€œRegressionâ€ and â€œCorrelationâ€ ?Note: y is dependent variable and x is independent variable.
A.	The relationship is symmetric between x and y in both.
B.	The relationship is not symmetric between x and y in both.
C.	The relationship is not symmetric between x and y in case of correlation but in case of regression it is symmetric.
D.	The relationship is symmetric between x and y in case of correlation but in case of regression it is not symmetric.
Answer» E.

Discussion

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

Discussion

82.	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» C. 3

Discussion

83.	______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» B. Creating sub-model to predict those features

Discussion

84.	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» D. None of the Mentioned

Discussion

85.	Â 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» B. lr(formula, data)

Discussion

86.	In the mathematical Equation of Linear Regression Yâ€„=â€„Î²1 + Î²2X + Ïµ, (Î²1, Î²2) refers to __________
A.	(X-intercept, Slope)
B.	(Slope, X-Intercept)
C.	(Y-Intercept, Slope)
D.	(slope, Y-Intercept)
Answer» D. (slope, Y-Intercept)

Discussion

87.	Lets say, a Linear regression model perfectly fits the training data (train error is zero). Now, Which of the following statement is true?
A.	you will always have test error zero
B.	you can not have test error zero
C.	none of the above
Answer» D.

Discussion

88.	which of the following step / assumption in regression modeling impacts the trade- off between under-fitting and over-fitting the most.
A.	the polynomial degree
B.	whether we learn the weights by matrix inversion or gradient descent
C.	the use of a constant-term
Answer» B. whether we learn the weights by matrix inversion or gradient descent

Discussion

89.	Can we calculate the skewness of variables based on mean and median?
A.	true
B.	false
Answer» C.

Discussion

90.	Which of the following statement(s) can be true post adding a variable in a linear regression model?1. R-Squared and Adjusted R-squared both increase2. R- Squared increases and Adjusted R-
A.	1 and 2
B.	1 and 3
C.	2 and 4
D.	none of the above
Answer» B. 1 and 3

Discussion

91.	Conditional probability is a measure of the probability of an event given that another event has already occurred.
A.	true
B.	false
Answer» B. false

Discussion

92.	What is/are true about kernel in SVM?1. Kernel function map low dimensional data to high dimensional space2. Its a similarity function
A.	1
B.	2
C.	1 and 2
D.	none of these
Answer» D. none of these

Discussion

93.	Suppose you are building a SVM model on data X. The data X can be error prone which means that you should not trust any specific data point too much. Now think that you want to build a SVM model which has quadratic kernel function of polynomial degree 2 that uses Slack variable C as one of its hyper parameter.What would happen when you use very small C (C~0)?
A.	misclassification would happen
B.	data will be correctly classified
C.	cant say
D.	none of these
Answer» B. data will be correctly classified

Discussion

94.	If you remove the non-red circled points from the data, the decision boundary will
A.	true
B.	false
Answer» C.

Discussion

95.	How do you handle missing or corrupted data in a dataset?
A.	a. drop missing rows or columns
B.	b. replace missing values with mean/median/mode
C.	c. assign a unique category to missing values
D.	d. all of the above
Answer» E.

Discussion

96.	The SVMs are less effective when:
A.	the data is linearly separable
B.	the data is clean and ready to use
C.	the data is noisy and contains overlapping points
Answer» D.

Discussion

97.	If there is only a discrete number of possible outcomes called Â Â Â Â Â Â Â Â Â .
A.	modelfree
B.	categories
C.	prediction
D.	none of above
Answer» C. prediction

Discussion

98.	Some people are using the term Â Â Â Â Â Â instead of prediction only to avoid the weird idea that machine learning is a sort of modern magic.
A.	inference
B.	interference
C.	accuracy
D.	none of above
Answer» B. interference

Discussion

99.	The term Â Â Â Â Â Â Â Â Â Â can be freely used, but with the same meaning adopted in physics or system theory.
A.	accuracy
B.	cluster
C.	regression
D.	prediction
Answer» E.

Discussion

100.	Common deep learning applications / problems can also be solved using
A.	real-time visual object identification
B.	classic approaches
C.	automatic labeling
D.	bio-inspired adaptive systems
Answer» C. automatic labeling

Discussion

Explore topic-wise MCQs in Computer Science Engineering (CSE).

If there is only a discrete number of possible outcomes called _____.

Some people are using the term ___ instead of prediction only to avoid the weird idea that machine learning is a sort of modern magic.

The term _____ can be freely used, but with the same meaning adopted in physics or system theory.

Common deep learning applications / problems can also be solved using____

what is the function of â€˜Unsupervised Learningâ€™?

Letâ€™s say, a â€œLinear regressionâ€ model perfectly fits the training data (train error is zero). Now, Which of the following statement is true?

In a linear regression problem, we are using â€œR-squaredâ€ to measure goodness-of-fit. We add a feature in linear regression model and retrain the same model.Which of the following option is true?

Which of the following is true about â€œRidgeâ€ or â€œLassoâ€ regression methods in case of feature selection?

SVM can solveÂ linearÂ and non-linearÂ problems

The objective of the support vector machine algorithm is to find a hyperplane in an N-dimensional space(N â€” the number of features) that distinctly classifies the data points.

Hyperplanes are _____________boundaries that help classify the data points.

The _____of the hyperplane depends upon the number of features.

SVMÂ algorithmsÂ useÂ a set of mathematical functions that are defined as theÂ kernel.

Which of the following isÂ notÂ supervised learning?

What is the purpose of performing cross-validation?

______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 impose some constraints to a clustering algorithm.

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_____.

Â If two variables are correlated, is it necessary that they have a linear relationship?

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 approach also allows simpler algorithms called _____

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

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

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

Â Â Â Â Â Â Â Â Â Â Â allows exploiting the natural sparsity of data while extracting principal components.

Which of the following option is true regarding â€œRegressionâ€ and â€œCorrelationâ€ ?Note: y is dependent variable and x is independent variable.

_____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________.

Â Function used for linear regression in R is __________

In the mathematical Equation of Linear Regression Yâ€„=â€„Î²1 + Î²2X + Ïµ, (Î²1, Î²2) refers to __________

Lets say, a Linear regression model perfectly fits the training data (train error is zero). Now, Which of the following statement is true?

which of the following step / assumption in regression modeling impacts the trade- off between under-fitting and over-fitting the most.

Can we calculate the skewness of variables based on mean and median?

Which of the following statement(s) can be true post adding a variable in a linear regression model?1. R-Squared and Adjusted R-squared both increase2. R- Squared increases and Adjusted R-

Conditional probability is a measure of the probability of an event given that another event has already occurred.

What is/are true about kernel in SVM?1. Kernel function map low dimensional data to high dimensional space2. Its a similarity function

If you remove the non-red circled points from the data, the decision boundary will

How do you handle missing or corrupted data in a dataset?

The SVMs are less effective when:

If there is only a discrete number of possible outcomes called Â Â Â Â Â Â Â Â Â .

Some people are using the term Â Â Â Â Â Â instead of prediction only to avoid the weird idea that machine learning is a sort of modern magic.

The term Â Â Â Â Â Â Â Â Â Â can be freely used, but with the same meaning adopted in physics or system theory.

Common deep learning applications / problems can also be solved using

Letâ€™s say, a â€œLinear regressionâ€ model perfectly fits the training data (train error is zero). Now, Which of the following statement is true?

In a linear regression problem, we are using â€œR-squaredâ€ to measure goodness-of-fit. We add a feature in linear regression model and retrain the same model.Which of the following option is true?

Which of the following is true about â€œRidgeâ€ or â€œLassoâ€ regression methods in case of feature selection?

Which of the following option is true regarding â€œRegressionâ€ and â€œCorrelationâ€ ?Note: y is dependent variable and x is independent variable.