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Classification Models

Total questions: 10

Worksheet time: 5mins

Name
Class
Date
1.

Which of the following is example of Classification Model?

a)

Stock Price Prediction

b)

Spam Filter

c)

Movie Recommendation

d)

Disease Prediction

2.

A table used to A table use to measure how well a classification model is performing

a)
accuracy table
b)
performance graph
c)
confusion matrix
d)
ROC curve
3.

The number of instances that were correctly predicted as negative

a)
False Negatives
b)
True Negatives
c)
False Positives
d)
True Positives
4.

The rate at which a model correctly predicts positive instances

a)
Specificity
b)
F1 Score
c)
Precision
d)
Recall
5.

Proportion of true positives detected out of all the actual positive instances.

a)
Recall
b)
Specificity
c)
F1 Score
d)
Precision
6.

An ____________ is computed as the harmonic mean of precision and recall.

a)
Specificity
b)
Accuracy
c)
F1 score
d)
AUC score
7.

What is the formula to calculate Recall?

a)

True Positives / (True Positives + False Negatives)

b)

True Positives / (True Positives + False Positives)

c)

(True Positives + False Positives) / Total Instances

d)

True Negatives / (True Negatives + False Positives)

8.

In a binary classification problem, if a model has a Recall of 0.85, what does this indicate?

a)

The model has a high rate of false positives

b)

The model correctly identifies 85% of actual negatives

c)

The model is perfectly accurate

d)

The model correctly identifies 85% of actual positives

9.

What is the primary difference between accuracy and precision in a classification model?

a)

Accuracy is calculated using true negatives, while precision is calculated using true positives.

b)

Accuracy measures overall correctness, while precision measures the correctness of positive predictions.

c)

Accuracy is more important than precision in all cases.

d)

Precision is always higher than accuracy.

10.

In the context of error calculation, what does a Type I error represent?

a)

Correctly accepting a false null hypothesis

b)

Correctly rejecting a true null hypothesis

c)

Incorrectly accepting a false null hypothesis

d)

Incorrectly rejecting a true null hypothesis