#0187

Mean Absolute Error

EasyML15 min10 XP

Problem

Given y_true and y_pred, return the mean absolute error.

Why This Matters

MAE is a plain-English regression metric: on average, how far are predictions from the true value?

Function Signature

def mean_absolute_error(y_true, y_pred):

Examples

Example 1

Inputy_true = [3, -1, 2], y_pred = [2, 1, 2]

Output1.0

Absolute errors are 1, 2, 0, and their average is 1.

Constraints

Return the exact requested result.
Handle common edge cases.
Keep the solution readable before optimizing.

CodePython

def mean_absolute_error(y_true, y_pred):
    """
    Input example: y_true = [3, -1, 2], y_pred = [2, 1, 2]
    Return: 1.0
    Return the value from the function so the tests can compare it. Do not print unless the prompt asks.
    """
    # write your code here
    return 0.0

def mean_absolute_error(y_true, y_pred):
    """
    Input example: y_true = [3, -1, 2], y_pred = [2, 1, 2]
    Return: 1.0
    Return the value from the function so the tests can compare it. Do not print unless the prompt asks.
    """
    # write your code here
    return 0.0

Visible browser tests run here when available.

Testcases1 visible / 3 hidden categories

Core example

Inputy_true = [3, -1, 2], y_pred = [2, 1, 2]

Expected1.0

Absolute errors are 1, 2, 0, and their average is 1.

Hidden Test Categories

minimal inputrepeated valueslarger realistic input