607 + Mcqs in Machine Learning Page 10 McqOptions

451.	Neural networks
A.	optimize a convex cost function
B.	always output values between 0 and 1
C.	can be used for regression as well as classification
D.	all of the above
Answer» D. all of the above

Discussion

452.	The difference between the actual Y value and the predicted Y value found using a regression equation is called the
A.	slope
B.	residual
C.	outlier
D.	scatter plot
Answer» B. residual

Discussion

453.	MLE estimates are often undesirable because
A.	they are biased
B.	they have high variance
C.	they are not consistent estimators
D.	none of the above
Answer» C. they are not consistent estimators

Discussion

454.	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?
A.	12
B.	24
C.	48
D.	72
Answer» E.

Discussion

455.	Based on survey , it was found that the probability that person like to watch serials is 0.25 and the probability that person like to watch netflix series is 0.43. Also the probability that person like to watch serials and netflix sereis is 0.12. what is the probability that a person doesn't like to watch either?
A.	0.32
B.	0.2
C.	0.44
D.	0.56
Answer» D. 0.56

Discussion

456.	Which of the following method is used for multiclass classification?
A.	one vs rest
B.	loocv
C.	all vs one
D.	one vs another
Answer» B. loocv

Discussion

457.	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]
A.	0.75
B.	0.97
C.	0.95
D.	0.85
Answer» C. 0.95

Discussion

458.	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
A.	1 is true, 2 is false
B.	1 is false, 2 is true
C.	1 is true, 2 is true
D.	1 is false, 2 is false
Answer» D. 1 is false, 2 is false

Discussion

459.	In SVM, RBF kernel with appropriate parameters to perform binary classification where the data is non-linearly seperable. In this scenario
A.	the decision boundry in the transformed feature space in non-linear
B.	the decision boundry in the transformed feature space in linear
C.	the decision boundry in the original feature space in not considered
D.	the decision boundry in the original feature space in linear
Answer» C. the decision boundry in the original feature space in not considered

Discussion

460.	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
A.	we can still classify the data correctly for given setting of hyper parameter c
B.	we can not classify the data correctly for given setting of hyper parameter c
C.	we can not classify the data at all
D.	data can be classified correctly without any impact of c
Answer» B. we can not classify the data correctly for given setting of hyper parameter c

Discussion

461.	The soft margin SVM is more preferred than the hard-margin SVM when-
A.	the data is linearly seperable
B.	the data is noisy and contains overlapping points
C.	the data is not noisy and linearly seperable
D.	the data is noisy and linearly seperable
Answer» C. the data is not noisy and linearly seperable

Discussion

462.	Which of the following is a categorical data?
A.	branch of bank
B.	expenditure in rupees
C.	prize of house
D.	weight of a person
Answer» B. expenditure in rupees

Discussion

463.	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.
A.	true, false
B.	false, true
C.	true,true
D.	false,false
Answer» D. false,false

Discussion

464.	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?
A.	precision
B.	recall
C.	call
D.	score
Answer» B. recall

Discussion

465.	Which of the following is not a kernel method in SVM?
A.	linear kernel
B.	polynomial kernel
C.	rbf kernel
D.	nonlinear kernel
Answer» B. polynomial kernel

Discussion

466.	Which of the following are components of generalization Error?
A.	bias
B.	vaiance
C.	both of them
D.	none of them
Answer» D. none of them

Discussion

467.	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» C. 0.9

Discussion

468.	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» D. option 1 and option 2

Discussion

469.	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» C. polynomial kernel

Discussion

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

Discussion

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

Discussion

472.	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 arenâ€™t good for imbalanced class problems.
A.	1 and 3
B.	1 and 4
C.	2 and 3
D.	2 and 4
Answer» B. 1 and 4

Discussion

473.	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» D. 20 perentage

Discussion

474.	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» D. outliers should be part of the test dataset but should not be present in the training data

Discussion

475.	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 âš‚ s2
B.	s1 may not be a subset of s2
C.	w1 = w2
D.	all of the above
Answer» C. w1 = w2

Discussion

476.	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» E.

Discussion

477.	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» C. hard-margin svm

Discussion

478.	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 â€œblack boxesâ€
D.	should be avoided altogether.
Answer» D. should be avoided altogether.

Discussion

479.	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» C. the model would not be affected by distance of points from hyperplane for modeling

Discussion

480.	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» C. decrease the model complexity

Discussion

481.	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» C. the training was not well regularized

Discussion

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

Discussion

483.	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» D. you will try to reduce the features

Discussion

484.	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» B. high-degree polynomial features

Discussion

485.	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» D. t is a model trained using unsupervised learning. it can be used for classification but not for regression.

Discussion

486.	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» E.

Discussion

487.	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» D. ridge has smaller bias, smaller variance

Discussion

488.	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» E.

Discussion

489.	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» E.

Discussion

490.	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» C. only changes very slowly, taking a period of several seconds to make large adjustments

Discussion

491.	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» B. to transform the problem from regression to classification

Discussion

492.	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 â€“ it can also output intermediate values as well
D.	canâ€™t say
Answer» B. false

Discussion

493.	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» D. none of the above

Discussion

494.	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» B. the svm allows high amount of error in classification

Discussion

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

Discussion

496.	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» B. linear logistic regression

Discussion

497.	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» D. option 1 and option 2

Discussion

498.	Support Vector Machine is
A.	logical model
B.	proababilistic model
C.	geometric model
D.	none of the above
Answer» D. none of the above

Discussion

499.	Machine learning techniques differ from statistical techniques in that machine learning methods
A.	typically assume an underlying distribution for the data.
B.	are better able to deal with missing and noisy data.
C.	are not able to explain their behavior.
D.	have trouble with large-sized datasets.
Answer» C. are not able to explain their behavior.

Discussion

500.	This unsupervised clustering algorithm terminates when mean values computed for the current iteration of the algorithm are identical to the computed mean values for the previous iteration.
A.	agglomerative clustering
B.	conceptual clustering
C.	k-means clustering
D.	expectation maximization
Answer» D. expectation maximization

Discussion

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

Neural networks

The difference between the actual Y value and the predicted Y value found using a regression equation is called the

MLE estimates are often undesirable because

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?

Which of the following method is used for multiclass classification?

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

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

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

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

Which of the following is a categorical data?

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.

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 of the following is not a kernel method in SVM?

Which of the following are components of generalization Error?

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

The SVMs are less effective when

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

Type of dataset available in Supervised Learning is

Perceptron Classifier is

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

Which statement about outliers is true?

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

Wrapper methods are hyper-parameter selection methods that

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

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?

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.

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

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?

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

How can SVM be classified?

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

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

The kernel trick

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

The firing rate of a neuron

What is the purpose of the Kernel Trick?

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 are support vectors?

What do you mean by a hard margin?

Impact of high variance on the training set ?

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

In multiclass classification number of classes must be

Support Vector Machine is

Machine learning techniques differ from statistical techniques in that machine learning methods

This unsupervised clustering algorithm terminates when mean values computed for the current iteration of the algorithm are identical to the computed mean values for the previous iteration.