Learn how to calculate the F1 score for a model with precision 0 and recall 0.5. Understand why the F1 score equals 0 and its implications for model performance metrics.
Table of Contents
Question
You are using performance metrics to validate a model. The precision and recall for the model is 0 and 0.5. What will be the F1 score for the model?
A. 0
B. 1
C. 2
D. 0.5
Answer
A. 0
Explanation
The F1 score is a harmonic mean of precision and recall, designed to balance the trade-off between these two metrics.
The F1 score evaluates a model’s balance between precision (how many predicted positives are actually correct) and recall (how many actual positives are correctly predicted). When precision is 0, it means none of the predicted positives are correct, rendering the harmonic mean 0, regardless of recall’s value.
Thus, the correct answer to the question is:
A. 0
This result highlights that the model fails completely in terms of precision, making its overall performance poor despite having some recall.
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