Machine Learning Set 4 MCQs

Q1 | 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)?

2

3

n

n+1

Q2 | Let S1 and S2 be the set of support vectors and w1 and w2 be the learnt weight vectors for a linearlyseparable problem using hard and soft margin linear SVMs respectively. Which of the following are correct?

s1 ⚂ s2

s1 may not be a subset of s2

w1 = w2

all of the above

Q3 | Which statement about outliers is true?

outliers should be part of the training dataset but should not be present in the test data

outliers should be identified and removed from a dataset

the nature of the problem determines how outliers are used

outliers should be part of the test dataset but should not be present in the training data

Q4 | If TP=9 FP=6 FN=26 TN=70 then Error rate will be

45 percentage

99 percentage

28 percentage

20 perentage

Q5 | 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.

1 and 3

1 and 4

2 and 3

2 and 4

Q6 | 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?

large datasets

small datasets

medium sized datasets

size does not matter

Q7 | Perceptron Classifier is

unsupervised learning algorithm

semi-supervised learning algorithm

supervised learning algorithm

soft margin classifier

Q8 | Type of dataset available in Supervised Learning is

unlabeled dataset

labeled dataset

csv file

excel file

Q9 | which among the following is the most appropriate kernel that can be used with SVM to separate the classes.

linear kernel

gaussian rbf kernel

polynomial kernel

option 1 and option 3

Q10 | The SVMs are less effective when

the data is linearly separable

the data is clean and ready to use

the data is noisy and contains overlapping points

option 1 and option 2

Q11 | Suppose you are using RBF kernel in SVM with high Gamma value. What does this signify?

the model would consider even far away points from hyperplane for modeling

the model would consider only the points close to the hyperplane for modeling

the model would not be affected by distance of points from hyperplane for modeling

opton 1 and option 2

Q12 | What is the precision value for following confusion matrix of binary classification?

0.91

0.09

0.9

0.95

Q13 | Which of the following are components of generalization Error?

bias

vaiance

both of them

none of them

Q14 | Which of the following is not a kernel method in SVM?

linear kernel

polynomial kernel

rbf kernel

nonlinear kernel

Q15 | 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?

precision

recall

call

score

Q16 | 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.

true, false

false, true

true,true

false,false

Q17 | Which of the following is a categorical data?

branch of bank

expenditure in rupees

prize of house

weight of a person

Q18 | The soft margin SVM is more preferred than the hard-margin SVM when-

the data is linearly seperable

the data is noisy and contains overlapping points

the data is not noisy and linearly seperable

the data is noisy and linearly seperable

Q19 | 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

we can still classify the data correctly for given setting of hyper parameter c

we can not classify the data correctly for given setting of hyper parameter c

we can not classify the data at all

data can be classified correctly without any impact of c

Q20 | In SVM, RBF kernel with appropriate parameters to perform binary classification where the data is non-linearly seperable. In this scenario

the decision boundry in the transformed feature space in non-linear

the decision boundry in the transformed feature space in linear

the decision boundry in the original feature space in not considered

the decision boundry in the original feature space in linear

Q21 | 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

1 is true, 2 is false

1 is false, 2 is true

1 is true, 2 is true

1 is false, 2 is false

Q22 | 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]

0.75

0.97

0.95

0.85

Q23 | Which of the following method is used for multiclass classification?

one vs rest

loocv

all vs one

one vs another

Q24 | 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?

0.32

0.2

0.44

0.56

Q25 | 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?

12

24

48

72