Calculate F1 Score from Predicted and True Labels

Problem

Calculate the F1 score given predicted labels and true labels. The F1 score is the harmonic mean of precision and recall, providing a single metric that balances both false positives and false negatives.

Solution

def f1_score(y_true: list[int], y_pred: list[int]) -> float:
    tp = sum(1 for t, p in zip(y_true, y_pred) if t == 1 and p == 1)
    fp = sum(1 for t, p in zip(y_true, y_pred) if t == 0 and p == 1)
    fn = sum(1 for t, p in zip(y_true, y_pred) if t == 1 and p == 0)

    precision = tp / (tp + fp) if (tp + fp) > 0 else 0.0
    recall = tp / (tp + fn) if (tp + fn) > 0 else 0.0

    if precision + recall == 0:
        return 0.0
    return round(2 * precision * recall / (precision + recall), 4)

Explanation

1. True Positives (TP): Count cases where both true and predicted are 1.
2. False Positives (FP): Count cases where true is 0 but predicted is 1.
3. False Negatives (FN): Count cases where true is 1 but predicted is 0.
4. Precision = TP / (TP + FP): Of all positive predictions, how many are correct.
5. Recall = TP / (TP + FN): Of all actual positives, how many were found.
6. F1 = 2 precision recall / (precision + recall): The harmonic mean balances precision and recall.

Complexity

• Time: O(n) where n is the number of samples
• Space: O(1)