Easy

Precision Score

~12 mincode completion

Implement . Assume at least one positive prediction exists.

Examples

Input
precision([1, 0, 1, 0, 1], [1, 1, 1, 0, 0])
Output
0.66667
Input
precision([1, 1, 1, 0], [1, 1, 0, 0])
Output
1
Input
precision([0, 0, 1, 1], [1, 1, 1, 0])
Output
0.33333

Hints

Hint 1

Sum with , and check which axis you are summing over.

Hint 2

Watch for this: confused fp and fn.

Requirements

  • y_true: Ground truth binary labels (0 or 1)

  • y_pred: Predicted binary labels (0 or 1)

  • Return Precision score in [0, 1].

Constraints

  • Allowed library: NumPy only

  • Time limit: 200 ms, Memory: 64 MB

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Where this shows up

~12 min

8 employers weight this skill

3 AI product companies, 2 frontier labs, 1 health and bio company, 1 big tech firm, 1 autonomy company. Top match scores 93.

Python
import numpy as np

def precision(y_true: np.ndarray, y_pred: np.ndarray) -> float:
    """
    Compute precision = TP / (TP + FP).

    Args:
        y_true: Ground truth binary labels (0 or 1)
        y_pred: Predicted binary labels (0 or 1)

    Returns:
        Precision score in [0, 1].
    """
    # YOUR CODE HERE
    pass
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