Classification QUIZ (MCQ QUESTIONS AND ANSWERS)

Total Correct: 0

Time:20:00

Question: 1

Which of the following metrics are used to evaluate classification models?

Area under the ROC curve

F1 score

Confusion matrix

All of the above

Question: 2

Which one is a classification algorithm?

Logistic regression

Linear regression

Polynomial regression

None

Question: 3

Classification is-

Unsupervised learning

Reinforcement learning

Supervised learning

None

Question: 4

You have a dataset of different flowers containing their petal lengths and color. Your model has to predict the type of flower for given petal lengths and color. This is a-

Regression task

Classification task

Clustering task

None

Question: 5

A classifier-

Inputs a vector of continuous values and outputs a single discrete value

Inputs a vector of discrete values and outputs a single discrete value

Both A and B

None

Question: 6

Classification is appropriate when you-

Try to predict a continuous valued output

Try to predict a class or discrete output

Both A and B for different contexts

None

Question: 7

With the help of a confusion matrix, we can compute-

Recall

Precision

Accuracy

All of the above

Question: 8

What does recall refer to in classification?

The proportion of all the relevant data points

The proportion of only the correct data points

The proportion of all data points

None

Question: 9

False negatives are-

Predicted negatives that are actually positives

Predicted positives that are actually negatives

Predicted negatives that are actually negatives

Predicted positives that are actually positives

Question: 10

Suppose your classification model predicted true for a class which actual value was false. Then this is a-

False positive

False negative

True positive

True negative

Question: 11

The false negative value is 5 and the true positive value is 20. What will be the value of recall-

0.2

0.6

0.8

0.3

Question: 12

The true positive value is 10 and the false positive value is 15. Calculate the value of precision-

0.6

0.4

0.5

None

Question: 13

If the precision is 0.6 and the recall value is 0.4. What will be the f measure?

0.5

0.6

0.4

0.3

Question: 14

Which one is a different algorithm?

Logistic Regression

Support Vector Regression

Polynomial Regression

None

Question: 15

What is a support vector?

The distance between any two data points

The average distance between all the data points

The distance between two boundary data points

The minimum distance between any two data points

Question: 16

Which of the following is a lazy learning algorithm?

SVM

KNN

Decision tree

All of the above

Question: 17

Which of the following is not a lazy learning algorithm?

SVM

Decision tree

Random forest

All of the above

Question: 18

What is the most widely used distance metric in KNN?

Euclidean distance

Manhattan distance

Perpendicular distance

All of the above

Question: 19

Which of the following is the best algorithm for text classification?

KNN

Decision tree

Random forest

Naive Bayes

Question: 20

What does k stand for in the KNN algorithm?

Number of neighbors

Number of output classes

Number of input features

None

Question: 21

Support Vector Machine is-

a discriminative classifier

a lazy learning classifier

a probabilistic classifier

None

Question: 22

What are hyperplanes?

Decision boundaries

Decision functions

Mapping functions

None

Question: 23

What is a kernel?

A function that calculates the distance of two boundary data points

A function that maps the value from one dimension to the other

A function that predicts the output value of a regression

None

Question: 24

Which of the following is not a kernel?

Polynomial Kernel

Gaussian Kernel

Sigmoid Kernel

None

Question: 25

Why Naive Bayes is called naive?

Because its assumption may or may not true

Because it’s a bad classifier

The accuracy is very poor

All of the above

Question: 26

For two events A and B, the Bayes theorem will be-

P(A | B) = P(B) * P(B | A) / P(A)

P(A | B) = P(A) * P(B | A) / P(B)

P(A | B) = P(B) * P(A | B) / P(A)

P(A | B) = P(A) * P(A | B) / P(B)

Question: 27

How does a decision tree work?

Minimizes the information gain and maximizes the entropy

Maximizes the information gain and minimizes the entropy

Minimizes the information gain and minimizes the entropy

Maximizes the information gain and maximizes the entropy

Question: 28

Suppose you have a dataset that is randomly distributed. What will be the best algorithm for that dataset?

Support vector machine

Naive Bayes

K nearest neighbors

Decision tree

Question: 29

Which pair of the algorithms are similar in operation?

SVM and KNN

Decision tree and Random forest

SVM and Naive Bayes

All of the above

Question: 30

Which metric is not used for evaluating classification models?

AUC ROC score

Accuracy

Mean absolute error

Precision

Classification QUIZ (MCQ QUESTIONS AND ANSWERS)

TIME:20:00

Time:20:00

Which of the following metrics are used to evaluate classification models?

Which one is a classification algorithm?

Classification is-

You have a dataset of different flowers containing their petal lengths and color. Your model has to predict the type of flower for given petal lengths and color. This is a-

A classifier-

Classification is appropriate when you-

With the help of a confusion matrix, we can compute-

What does recall refer to in classification?

False negatives are-

Suppose your classification model predicted true for a class which actual value was false. Then this is a-

The false negative value is 5 and the true positive value is 20. What will be the value of recall-

The true positive value is 10 and the false positive value is 15. Calculate the value of precision-

If the precision is 0.6 and the recall value is 0.4. What will be the f measure?

Which one is a different algorithm?

What is a support vector?

Which of the following is a lazy learning algorithm?

Which of the following is not a lazy learning algorithm?

What is the most widely used distance metric in KNN?

Which of the following is the best algorithm for text classification?

What does k stand for in the KNN algorithm?

Support Vector Machine is-

What are hyperplanes?

What is a kernel?

Which of the following is not a kernel?

Why Naive Bayes is called naive?

For two events A and B, the Bayes theorem will be-

How does a decision tree work?

Suppose you have a dataset that is randomly distributed. What will be the best algorithm for that dataset?

Which pair of the algorithms are similar in operation?

Which metric is not used for evaluating classification models?