Implement Recall Metric in Binary Classification Task: Implement Recall in Binary Classification Your task is to implement the recall metric in a binary classification setting. Recall is a performance measure that evaluates how effectively a machine learning model identifies positive instances from all the actual positive cases in a dataset. You need to write a function recall(y_true, y_pred) that calculates the recall metric. The function should accept two inputs: y_true: A list of true binary labels (0 or 1) for the dataset. y_pred: A list of predicted binary labels (0 or 1) from the model. Your function should return the recall value rounded to three decimal places. If the denominator (TP + FN) is zero, the recall should be 0.0 to avoid division by zero. Example: Input: import numpy as np y_true = np.array([1, 0, 1, 1, 0, 1]) y_pred = np.array([1, 0, 1, 0, 0, 1]) print(recall(y_true, y_pred)) Output: # 0.75