ML Coding
Back to problems

Metrics ยท Easy

precision_at_k

Implement a ranking metric that measures how many relevant examples appear in the top k results after sorting by model score.

Task

Write precision_at_k(labels, scores, k). The input labels are binary values where 1 means relevant and 0 means not relevant. Sort examples by descending score and return the mean label among the top k examples.

Function Signature

def precision_at_k(labels: list[int], scores: list[float], k: int) -> float:
    pass

Rules

  • If k <= 0, return 0.0.
  • If k is larger than the number of examples, use all examples.
  • If two scores tie, keep the original input order.
  • Assume labels and scores have the same length.

Examples

labels = [0, 1, 1, 0]
scores = [0.2, 0.9, 0.4, 0.7]

precision_at_k(labels, scores, 2)
# 0.5

precision_at_k(labels, scores, 3)
# 0.6666666667