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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51.	In multiclass classification number of classes must be
A.	less than two
B.	equals to two
C.	greater than two
D.	option 1 and option 2
Answer» C. greater than two

52.	Which of the following can only be used when training data are linearlyseparable?
A.	linear hard-margin svm
B.	linear logistic regression
C.	linear soft margin svm
D.	the centroid method
Answer» A. linear hard-margin svm

53.	Impact of high variance on the training set ?
A.	overfitting
B.	underfitting
C.	both underfitting & overfitting
D.	depents upon the dataset
Answer» A. overfitting

54.	What do you mean by a hard margin?
A.	the svm allows very low error in classification
B.	the svm allows high amount of error in classification
C.	both 1 & 2
D.	none of the above
Answer» A. the svm allows very low error in classification

55.	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» A. selection of kernel

56.	What are support vectors?
A.	all the examples that have a non-zero weight ??k in a svm
B.	the only examples necessary to compute f(x) in an svm.
C.	all of the above
D.	none of the above
Answer» C. all of the above

57.	A perceptron adds up all the weighted inputs it receives, and if it exceeds a certain value, it outputs a 1, otherwise it just outputs a 0.
A.	true
B.	false
C.	sometimes – it can also output intermediate values as well
D.	can’t say
Answer» A. true

58.	What is the purpose of the Kernel Trick?
A.	to transform the data from nonlinearly separable to linearly separable
B.	to transform the problem from regression to classification
C.	to transform the problem from supervised to unsupervised learning.
D.	all of the above
Answer» A. to transform the data from nonlinearly separable to linearly separable

60.	The firing rate of a neuron
A.	determines how strongly the dendrites of the neuron stimulate axons of neighboring neurons
B.	is more analogous to the output of a unit in a neural net than the output voltage of the neuron
C.	only changes very slowly, taking a period of several seconds to make large adjustments
D.	can sometimes exceed 30,000 action potentials per second
Answer» B. is more analogous to the output of a unit in a neural net than the output voltage of the neuron

61.	Which of the following evaluation metrics can not be applied in case of logistic regression output to compare with target?
A.	auc-roc
B.	accuracy
C.	logloss
D.	mean-squared-error
Answer» D. mean-squared-error

62.	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 misclassification and simplicity of the model
D.	none of the above
Answer» C. the tradeoff between misclassification and simplicity of the model

63.	The kernel trick
A.	can be applied to every classification algorithm
B.	is commonly used for dimensionality reduction
C.	changes ridge regression so we solve a d ?? d linear system instead of an n ?? n system, given n sample points with d features
D.	exploits the fact that in many learning algorithms, the weights can be written as a linear combination of input points
Answer» D. exploits the fact that in many learning algorithms, the weights can be written as a linear combination of input points

64.	How does the bias-variance decomposition of a ridge regression estimator compare with that of ordinary least squares regression?
A.	ridge has larger bias, larger variance
B.	ridge has smaller bias, larger variance
C.	ridge has larger bias, smaller variance
D.	ridge has smaller bias, smaller variance
Answer» C. ridge has larger bias, smaller variance

68.	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 is best 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

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

65.	Which of the following are real world applications of the SVM?
A.	text and hypertext categorization
B.	image classification
C.	clustering of news articles
D.	all of the above
Answer» D. all of the above

66.	How can SVM be classified?
A.	it is a model trained using unsupervised learning. it can be used for classification and regression.
B.	it is a model trained using unsupervised learning. it can be used for classification but not for regression.
C.	it is a model trained using supervised learning. it can be used for classification and regression.
D.	t is a model trained using unsupervised learning. it can be used for classification but not for regression.
Answer» C. it is a model trained using supervised learning. it can be used for classification and regression.

67.	Which of the following can help to reduce overfitting in an SVM classifier?
A.	use of slack variables
B.	high-degree polynomial features
C.	normalizing the data
D.	setting a very low learning rate
Answer» A. use of slack variables

70.	You trained a binary classifier model which gives very high accuracy on the training data, but much lower accuracy on validation data. Which is false.
A.	this is an instance of overfitting
B.	this is an instance of underfitting
C.	the training was not well regularized
D.	the training and testing examples are sampled from different distributions
Answer» B. this is an instance of underfitting

71.	Suppose your model is demonstrating high variance across the different training sets. Which of the following is NOT valid way to try and reduce the variance?
A.	increase the amount of traning data in each traning set
B.	improve the optimization algorithm being used for error minimization.
C.	decrease the model complexity
D.	reduce the noise in the training data
Answer» B. improve the optimization algorithm being used for error minimization.

72.	Suppose you are using RBF kernel in SVM with high Gamma value. What does this signify?
A.	the model would consider even far away points from hyperplane for modeling
B.	the model would consider only the points close to the hyperplane for modeling
C.	the model would not be affected by distance of points from hyperplane for modeling
D.	none of the above
Answer» B. the model would consider only the points close to the hyperplane for modeling

73.	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

74.	Wrapper methods are hyper-parameter selection methods that
A.	should be used whenever possible because they are computationally efficient
B.	should be avoided unless there are no other options because they are always prone to overfitting.
C.	are useful mainly when the learning machines are “black boxes”
D.	should be avoided altogether.
Answer» C. are useful mainly when the learning machines are “black boxes”

75.	Which of the following methods can not achieve zero training error on any linearly separable dataset?
A.	decision tree
B.	15-nearest neighbors
C.	hard-margin svm
D.	perceptron
Answer» B. 15-nearest neighbors

76.	Suppose we train a hard-margin linear SVM on n > 100 data points in R2, yielding a hyperplane with exactly 2 support vectors. If we add one more data point and retrain the classifier, what is the maximum possible number of support vectors for the new hyperplane (assuming the n + 1 points are linearly separable)?
A.	2
B.	3
C.	n
D.	n+1
Answer» D. n+1

730+ Machine Learning (ML) Solved MCQs

In multiclass classification number of classes must be

Which of the following can only be used when training data are linearlyseparable?

Impact of high variance on the training set ?

What do you mean by a hard margin?

The effectiveness of an SVM depends upon:

What are support vectors?

A perceptron adds up all the weighted inputs it receives, and if it exceeds a certain value, it outputs a 1, otherwise it just outputs a 0.

What is the purpose of the Kernel Trick?

Which of the following can only be used when training data are linearlyseparable?

The firing rate of a neuron

Which of the following evaluation metrics can not be applied in case of logistic regression output to compare with target?

The cost parameter in the SVM means:

The kernel trick

How does the bias-variance decomposition of a ridge regression estimator compare with that of ordinary least squares regression?

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

How can SVM be classified?

Which of the following can help to reduce overfitting in an SVM classifier?

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 is best option would you more likely to consider iterating SVM next time?

What is/are true about kernel in SVM? 1. Kernel function map low dimensional data to high dimensional space 2. It’s a similarity function

You trained a binary classifier model which gives very high accuracy on the training data, but much lower accuracy on validation data. Which is false.

Suppose your model is demonstrating high variance across the different training sets. Which of the following is NOT valid way to try and reduce the variance?

Suppose you are using RBF kernel in SVM with high Gamma value. What does this signify?

Wrapper methods are hyper-parameter selection methods that

Which of the following methods can not achieve zero training error on any linearly separable dataset?

Let S1 and S2 be the set of support vectors and w1 and w2 be the learnt weight vectors for a linearly separable problem using hard and soft margin linear SVMs respectively. Which of the following are correct?

Which statement about outliers is true?

If TP=9 FP=6 FN=26 TN=70 then Error rate will be

he 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?

Perceptron Classifier is

Type of dataset available in Supervised Learning is

which among the following is the most appropriate kernel that can be used with SVM to separate the classes.

The SVMs are less effective when

Suppose you are using RBF kernel in SVM with high Gamma value. What does this signify?

What is the precision value for following confusion matrix of binary classification?

Which of the following are components of generalization Error?

Which of the following is not a kernel method in SVM?

During the treatement of cancer patients , the doctor needs to be very careful about which patients need to be given chemotherapy.Which metric should we use in order to decide the patients who should given chemotherapy?

Which one of the following is suitable? 1. When the hypothsis space is richer, overfitting is more likely. 2. when the feature space is larger , overfitting is more likely.

Which of the following is a categorical data?

The soft margin SVM is more preferred than the hard-margin SVM when-

In SVM which has quadratic kernel function of polynomial degree 2 that has slack variable C as one hyper paramenter. What would happen if we use very large value for C

In SVM, RBF kernel with appropriate parameters to perform binary classification where the data is non-linearly seperable. In this scenario

Which of the following is true about SVM? 1. Kernel function map low dimensional data to high dimensional space. 2. It is a similarity Function

What is the Accuracy in percentage based on following confusion matrix of three class classification. Confusion Matrix C= [14 0 0] [ 1 15 0] [ 0 0 6]

Which of the following method is used for multiclass classification?

A machine learning problem involves four attributes plus a class. The attributes have 3, 2, 2, and 2 possible values each. The class has 3 possible values. How many maximum possible different examples are there?

Let S1 and S2 be the set of support vectors and w1 and w2 be the learnt weight vectors for a linearly
separable problem using hard and soft margin linear SVMs respectively. Which of the following are correct?

What is the Accuracy in percentage based on following confusion matrix of three class classification.
Confusion Matrix C=
[14 0 0]
[ 1 15 0]
[ 0 0 6]

77.	Let S1 and S2 be the set of support vectors and w1 and w2 be the learnt weight vectors for a linearly separable problem using hard and soft margin linear SVMs respectively. Which of the following are correct?
A.	s1 ⚂ s2
B.	s1 may not be a subset of s2
C.	w1 = w2
D.	all of the above
Answer» B. s1 may not be a subset of s2

78.	Which statement about outliers is true?
A.	outliers should be part of the training dataset but should not be present in the test data
B.	outliers should be identified and removed from a dataset
C.	the nature of the problem determines how outliers are used
D.	outliers should be part of the test dataset but should not be present in the training data
Answer» C. the nature of the problem determines how outliers are used

79.	If TP=9 FP=6 FN=26 TN=70 then Error rate will be
A.	45 percentage
B.	99 percentage
C.	28 percentage
D.	20 perentage
Answer» C. 28 percentage

80.	Imagine, you are solving a classification problems with highly imbalanced class. The majority class is observed 99% of times in the training data. Your model has 99% accuracy after taking the predictions on test data. Which of the following is true in such a case? 1. Accuracy metric is not a good idea for imbalanced class problems. 2.Accuracy metric is a good idea for imbalanced class problems. 3.Precision and recall metrics are good for imbalanced class problems. 4.Precision and recall metrics aren’t good for imbalanced class problems.
A.	1 and 3
B.	1 and 4
C.	2 and 3
D.	2 and 4
Answer» A. 1 and 3

81.	he 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

82.	Perceptron Classifier is
A.	unsupervised learning algorithm
B.	semi-supervised learning algorithm
C.	supervised learning algorithm
D.	soft margin classifier
Answer» C. supervised learning algorithm

83.	Type of dataset available in Supervised Learning is
A.	unlabeled dataset
B.	labeled dataset
C.	csv file
D.	excel file
Answer» B. labeled dataset

84.	which among the following is the most appropriate kernel that can be used with SVM to separate the classes.
A.	linear kernel
B.	gaussian rbf kernel
C.	polynomial kernel
D.	option 1 and option 3
Answer» B. gaussian rbf kernel

85.	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
D.	option 1 and option 2
Answer» C. the data is noisy and contains overlapping points

87.	What is the precision value for following confusion matrix of binary classification?
A.	0.91
B.	0.09
C.	0.9
D.	0.95
Answer» B. 0.09