Easy

F1 Score

Easy

~15 min

code completion

F1 is the harmonic mean of precision and recall — a single metric that balances both:

F_{1} = \frac{2 \cdot precision \cdot recall}{precision + recall} = \frac{2 \cdot T P}{2 \cdot T P + F P + F N}

where:

TP: predicted positive, actually positive

FP: predicted positive, actually negative

FN: predicted negative, actually positive

F1 = 1.0 means perfect precision and recall. F1 = 0.0 means at least one is zero.

Your task:

Implement f1_score(y_true, y_pred). Assume 2*TP + FP + FN > 0.

Example Tests

2 TP, 1 FP, 1 FN: F1 = 0.667

Input: {"y_pred":[1,1,1,0,0],"y_true":[1,0,1,0,1]}

Expected: 0.66667

Perfect predictions: F1 = 1.0

Input: {"y_pred":[1,1,0,0],"y_true":[1,1,0,0]}

Expected: 1

All predictions wrong (FN only): F1 = 0.0

Input: {"y_pred":[0,0,0,0],"y_true":[1,1,1,0]}

Expected: 0

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