Calculate Precision, Recall, and F1

Q: Calculate Precision, Recall, and F1

This question evaluates understanding of binary classification evaluation metrics (precision, recall, F1) and the ability to compute confusion matrix counts from actual and predicted labels, situated in the Statistics & Math domain for Data Scientist roles.

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Question

You are given a list of binary classification outputs such as [{"actual": 1, "predicted": 0, "confidence": 0.93}, ...], where class 1 is the positive class. Using the provided actual and predicted labels, write Python or pseudocode to compute the confusion-matrix counts (TP, FP, TN, FN) and then calculate:

precision = TP / (TP + FP)
recall = TP / (TP + FN)
F1 = 2 * precision * recall / (precision + recall)

If the interviewer instead asks you to derive predicted from confidence, assume a decision threshold and explain how the metrics change when the threshold changes. Also describe how you would handle edge cases such as no predicted positives or no actual positives.

Calculate Precision, Recall, and F1

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