MLmediumEvaluation~10m

F1 Score Implementation

Problem

Implement the F1 score metric using only NumPy. The F1 score is the harmonic mean of precision and recall: F1 = 2 * (precision * recall) / (precision + recall), where precision = TP/(TP+FP) and recall = TP/(TP+FN).

Examples

Example 1

Input: y_true=[1, 0, 1, 1, 0], y_pred=[1, 0, 1, 0, 0]

Output: 0.8

TP=2, FP=0, FN=1 gives precision=1.0, recall=0.667, F1=0.8 / 1.2 = 0.66667

Constraints

›NumPy only (import numpy as np)
›Function must be named solution

Reference solution

Reference solution available after you attempt the question.

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Start a session on Mockbit #93. You'll get graded with specific critique when you submit.