Implement F-Score Calculation for Binary Classification 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. Example: 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 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