Confusion Matrix

Confusion Matrix

Instructions:

Number of questions: 10
Time limit: 20 minutes
Must be finished in one sitting. You cannot save and finish later.
Questions displayed per page: 1
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Will not let you finish with any questions unattempted.

1 / 10

What is the definition of a confusion matrix?

A table or report card that shows how well your classification algorithm is performing

A table or report card that shows how well your regression algorithm is performing

A table or report card that shows how well your clustering algorithm is performing

A table or report card that shows how well your unsupervised algorithm is performing

2 / 10

Which option correctly describes using a Confusion Matrix to evaluate a model on a predictive problem?

A confusion matrix is a performance measurement for machine learning classification problems where outputs are two classes only

A confusion matrix often used to describe the performance of a regression model on a set of data

The rare class is usually the class of more interest, and it is typically designated 1

A confusion matrix is useful for measuring Accuracy only for your models

3 / 10

Please fill in the table below on A, B, C, and D with correct answers:

A: True Negative, B: False Negative, C: False Positive, and D: True Positive

A: True Positive, B: False Positive, C: False Negative, and D: True Negative

A: True Positive, B: True Negative, C: False Positive, and D: False Negative

A: False Positive, B: False Negative, C: True Positive, and D: True Negative

4 / 10

In classification problems, the rare class is usually the class of more interested (e.g., the patients who have cancer). In this typical scenario, this rare class is typically designated:

Negative (0)

Positive (1)

Both negative (0) and positive (1)

None of the above

5 / 10

Which metric is commonly used in evaluating classification models?

Accuracy

Precision

Recall

Specificity

All of the above

6 / 10

Confusion matrix is a tabular display of the record counts by their predicted and actual classification status. Which is the structure of a confusion matrix for a binary case?

3 x 3 table

2 x 2 table

2 x 3 table

3 x 2 table

7 / 10

Which answer is correct in terms of using a confusion matrix for evaluating a model?

We can use it in a classification case with binary classes only

We can use it in regression cases

We can use it in a classification case with binary or multiple classes

None of the above

8 / 10

According to the table below, which option is incorrect?

This is a confusion matrix for evaluating a model's performance

This is a binary case in a classification problem (positive (1) or negative (0))

The classifier made a total of 200 predictions

Out of those predictions, 98 are true negative and 95 are true positive

9 / 10

Costco is building a customer counter system at both entry and exit with a computer vision algorithm to restrict the number of customers entering the store due to Covid-19. How would you design a metric to evaluate the model’s performance?

Using a confusion matrix with binary classes

Designating 1 (positive) to “customers detected”

Designating 0 (negative) to “no customers”

All of the above

10 / 10

You're building a new attention classifier for Zoom (a cloud platform for video and audio conferencing) because of kids not paying attention during online classes. So you're creating a scoring mechanism. For testing the classifier’s performance, you collected 1000 test records in total. 30 out of 500 tests, the classifier issued a warning (no attention) even when a student is paying attention in a meeting: so, a warning (no attention) was correctly issued 370 times. But 50 out of 500 times, there was no warning when a student was not paying attention. Please fill out a confusion matrix (A to H) below based on the information you collected.

A & C=Not paying attention, B & D=Paying attention, E=470, F=30, G=50, H=450

A & C=Not paying attention, B & D=Paying attention, E=450, F=50, G=30, H=470

A & C=Paying attention, B & D=Not paying attention, E=470, F=30, G=50, H=450

A & C=Paying attention, B & D=Not paying attention, E=450, F=50, G=30, H=47

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