The AI Interview - Master AI/ML Interviews

Implement a function to calculate the Mean Absolute Error (MAE) between two arrays of actual and predicted values. The MAE is a metric used to measure the average magnitude of errors in a set of predictions without considering their direction.

Your function should return the MAE as a float value.

Examples

Example 1:

Input: y_true = np.array([3, -0.5, 2, 7]), y_pred = np.array([2.5, 0.0, 2, 8])

Output: 0.5

Explanation: The MAE is the mean of absolute differences: (|3-2.5| + |-0.5-0| + |2-2| + |7-8|) / 4 = (0.5 + 0.5 + 0 + 1) / 4 = 0.5

Starter Code

import numpy as np

def mae(y_true, y_pred):
    """
    Calculate Mean Absolute Error between two arrays.

    Parameters:
        y_true (numpy.ndarray): Array of true values
        y_pred (numpy.ndarray): Array of predicted values

    Returns:
        float: Mean Absolute Error
    """
    # Your code here
    pass

Calculate Mean Absolute Error (MAE)

Examples

Starter Code