Compute Precision, Recall, and F1

Q: Compute Precision, Recall, and F1

This question evaluates competency in computing and interpreting binary classification metrics—precision, recall, and F1—along with understanding thresholding of confidence scores and handling edge cases like zero denominators.

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Question

You are given a list of binary classification outputs, where each record contains:

actual INT ( 0 or 1 )
predict INT ( 0 or 1 )
conf FLOAT, the model's confidence score for the positive class

Example record: [actual=1, predict=0, conf=0.93].

Write Python or pseudocode to compute the following metrics for the positive class over the full dataset:

precision
recall
F1 score

Also explain:

how to derive predict from conf if the interviewer asks you to apply a threshold instead of using the provided predicted labels
how to handle edge cases where a denominator becomes zero

Compute Precision, Recall, and F1

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