Classification QUIZ (MCQ QUESTIONS AND ANSWERS)

Area under the ROC curve

F1 score

Confusion matrix

All of the above

Logistic regression

Linear regression

Polynomial regression

None

Unsupervised learning

Reinforcement learning

Supervised learning

None

Regression task

Classification task

Clustering task

None

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

Try to predict a continuous valued output

Try to predict a class or discrete output

Both A and B for different contexts

None

Recall

Precision

Accuracy

All of the above

The proportion of all the relevant data points

The proportion of only the correct data points

The proportion of all data points

None

Predicted negatives that are actually positives

Predicted positives that are actually negatives

Predicted negatives that are actually negatives

Predicted positives that are actually positives

False positive

False negative

True positive

True negative

0.2

0.6

0.8

0.3

0.6

0.4

0.5

None

0.5

0.6

0.4

0.3

Logistic Regression

Support Vector Regression

Polynomial Regression

None

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

SVM

KNN

Decision tree

All of the above

SVM

Decision tree

Random forest

All of the above

Euclidean distance

Manhattan distance

Perpendicular distance

All of the above

KNN

Decision tree

Random forest

Naive Bayes

Number of neighbors

Number of output classes

Number of input features

None

a discriminative classifier

a lazy learning classifier

a probabilistic classifier

None

Decision boundaries

Decision functions

Mapping functions

None

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

Polynomial Kernel

Gaussian Kernel

Sigmoid Kernel

None

Because its assumption may or may not true

Because it’s a bad classifier

The accuracy is very poor

All of the above

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)

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

Support vector machine

Naive Bayes

K nearest neighbors

Decision tree

SVM and KNN

Decision tree and Random forest

SVM and Naive Bayes

All of the above

AUC ROC score

Accuracy

Mean absolute error

Precision