Precision and Recall Function | Dataford Interview Questions

For a binary classification model used in a 7-Eleven customer offer pipeline, calculate precision and recall from true labels and predicted probability scores. A score greater than or equal to the supplied threshold is classified as positive.

Implement precision_recall_at_threshold without using machine learning libraries.

Formal Specification

The function receives:

y_true: a list of integers containing only 0 and 1, where 1 is the positive class.
scores: a list of numeric model scores, with one score for each label.
threshold: a numeric cutoff.

Return a dictionary with numeric keys precision and recall, each represented as a floating-point value. If precision or recall has a zero denominator, return 0.0 for that metric.

Constraints

1 <= len(y_true) = len(scores) <= 10^5
Every value in y_true is either 0 or 1
Scores and threshold are numeric
0 <= threshold <= 1

Function Signature

def precision_recall_at_threshold(y_true, scores, threshold):

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