The AI Interview - Master AI/ML Interviews

Task: Implement F-Score Calculation for Binary Classification

Your task is to implement a function that calculates the F-Score for a binary classification task. The F-Score combines both Precision and Recall into a single metric, providing a balanced measure of a model's performance.

Write a function f_score(y_true, y_pred, beta) where:

y_true: A numpy array of true labels (binary).
y_pred: A numpy array of predicted labels (binary).
beta: A float value that adjusts the importance of Precision and Recall. When beta=1, it computes the F1-Score, a balanced measure of both Precision and Recall.

The function should return the F-Score rounded to three decimal places.

Examples

Example 1:

Input:

y_true = np.array([1, 0, 1, 1, 0, 1])
y_pred = np.array([1, 0, 1, 0, 0, 1])
beta = 1

print(f_score(y_true, y_pred, beta))

Output: 0.857

Explanation: The F-Score for the binary classification task is calculated using the true labels, predicted labels, and beta value.

Starter Code

import numpy as np

def f_score(y_true, y_pred, beta):
	"""
	Calculate F-Score for a binary classification task.

	:param y_true: Numpy array of true labels
	:param y_pred: Numpy array of predicted labels
	:param beta: The weight of precision in the harmonic mean
	:return: F-Score rounded to three decimal places
	"""
	pass