Exam DP-100 topic 3 question 31 discussion

Actual exam question from Microsoft's DP-100

Question #: 31
Topic #: 3

HOTSPOT -
You create a binary classification model to predict whether a person has a disease.
You need to detect possible classification errors.
Which error type should you choose for each description? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Hot Area:

Show Suggested Answer

Suggested Answer:

Box 1: True Positive -
A true positive is an outcome where the model correctly predicts the positive class

Box 2: True Negative -
A true negative is an outcome where the model correctly predicts the negative class.

Box 3: False Positive -
A false positive is an outcome where the model incorrectly predicts the positive class.

Box 4: False Negative -
A false negative is an outcome where the model incorrectly predicts the negative class.
Note: Let's make the following definitions:
"Wolf" is a positive class.
"No wolf" is a negative class.
We can summarize our "wolf-prediction" model using a 2x2 confusion matrix that depicts all four possible outcomes:
Reference:
https://developers.google.com/machine-learning/crash-course/classification/true-false-positive-negative

by Nugi at Aug. 14, 2020, 9:20 a.m.

Comments

Submit Cancel

lucazav

Highly Voted 3 years, 1 month ago

A mnemonic rule could be: - "Positive" and "Negative" are related to the output of the model, once the positive result is associated to an outcome by convention. - "True" and "False" are the result comparing the model output to the reality That said, given that is positive a person that has a disease by convention, "the model predict that a person has a disease (Positive), and the person doesn't have a disease (False with respect to the prediction): it's a False Positive

upvoted 19 times

...

Nugi

Highly Voted 3 years, 3 months ago

True Positive, True Negative, False Positive, False Negative.

upvoted 13 times

...

nokcha1006

Most Recent 6 months, 3 weeks ago

True Positive, True Negative, False Negative, False Positive. "Wolf" is a [P (Positives)] : Reality: A wolf threatened [T (True)] Reality: No wolf threatened. [F (False)] "No wolf" is a [N (Negatives)] : Reality: No wolf threatened. [T (True)] Reality: A wolf threatened [F (False)]

upvoted 1 times

...