The AI Interview - Master AI/ML Interviews

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 as a float. If the denominator (TP + FN) is zero, return 0.0 to avoid division by zero.

Examples

Example 1:

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

Explanation: There are 4 actual positive instances in y_true. The model correctly identified 3 of them, giving a recall of 0.75.

Starter Code

import numpy as np

def recall(y_true, y_pred):
    """
    Calculate the recall metric for binary classification.
    
    Args:
        y_true: Array of true binary labels (0 or 1)
        y_pred: Array of predicted binary labels (0 or 1)
    
    Returns:
        Recall value as a float
    """
    # Your code here
    pass

Implement Recall Metric in Binary Classification

Task: Implement Recall in Binary Classification

Examples

Starter Code