Click4Ai

69.

Easy

Implement the **F1 Score**, the harmonic mean of precision and recall.

Formula:

F1 = 2 * (Precision * Recall) / (Precision + Recall)

The F1 score balances precision and recall into a single metric. It is especially useful when you have imbalanced classes.

Write a function f1_score(y_true, y_pred) that computes precision and recall internally, then returns the F1 score.

Example:

y_true = [1, 0, 1, 1, 0, 1, 0, 1]

y_pred = [1, 0, 0, 1, 1, 1, 0, 1]

f1_score(y_true, y_pred) → 0.8

**Explanation:** Precision = 4/5 = 0.8, Recall = 4/5 = 0.8. F1 = 2 * 0.8 * 0.8 / (0.8 + 0.8) = 0.8.

Constraints:

  • Return 0.0 if both precision and recall are 0
  • Compute TP, FP, FN from the inputs directly
  • Return a float between 0.0 and 1.0

    • Test Cases

    Test Case 1
    Input: y_true=[1,0,1,1,0,1,0,1], y_pred=[1,0,0,1,1,1,0,1]
    Expected: 0.8
    Test Case 2
    Input: y_true=[1,1,1], y_pred=[1,1,1]
    Expected: 1.0
    Test Case 3
    Input: y_true=[1,1,0,0], y_pred=[0,0,1,1]
    Expected: 0.0
    + 2 hidden test cases