Classification Metrics Evaluation | Dataford Interview Questions

In InstaDeep's DeepChain model-evaluation workflow, implement a function that computes standard binary classification metrics from predicted labels and ground-truth labels. Return accuracy, precision, recall, and F1 score without using external machine-learning libraries.

Formal Specification

Given two lists, predictions and labels, containing binary values, and an integer positive_label, return a dictionary with the floating-point keys accuracy, precision, recall, and f1.

Use these definitions:

accuracy = (TP + TN) / N
precision = TP / (TP + FP)
recall = TP / (TP + FN)
f1 = 2 * precision * recall / (precision + recall)

If a metric has a zero denominator, return 0.0 for that metric. In particular, F1 is 0.0 when both precision and recall are zero. The input lists are guaranteed to have equal, nonzero lengths.

Constraints

1 <= len(predictions) = len(labels) <= 10^6
Each prediction and label is either 0 or 1
positive_label is either 0 or 1
The input lists are nonempty and have equal length
A metric with a zero denominator must be returned as 0.0

Function Signature

def compute_classification_metrics(predictions, labels, positive_label):

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