Which error type should you choose for each description?

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.

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Answer:

Explanation:

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 2×2 confusion matrix that depicts all four possible outcomes:

Reference: https://developers.google.com/machine-learning/crash-course/classification/true-false-positive-negative