Mean Absolute Error of Forecasts

Problem

Compute the mean absolute error between actual values and forecasts: (1/n) * sum(abs(actual[t] - forecast[t])).

Return 0.0 when the actual list is empty.

Hint

Take absolute values before averaging so positive and negative errors cannot cancel.

case 1

in: [[10,12,14],[11,11,11]]

expected: 1.6666666666666667

case 2

in: [[5,5],[3,7]]

expected: 2

case 3

in: [[1],[1]]

expected: 0

case 4

in: [[1,2,3,4],[2,2,2,2]]

expected: 1

Starter codePython

def mean_absolute_error(actual, forecast):
    # Your code here
    pass

4 test cases · run your code against them in the editor