Mean Absolute Percentage Error for Forecasts

Problem

Compute the mean absolute percentage error (MAPE) as a percentage: (100 / n) * sum(abs(actual[t] - forecast[t]) / abs(actual[t])).

All actual values are assumed to be nonzero. Return 0.0 when the actual list is empty.

Hint

Scale each error by the actual magnitude before averaging.

case 1

in: [[100,200],[110,190]]

expected: 7.500000000000001

case 2

in: [[50],[55]]

expected: 10

case 3

in: [[10,10],[10,10]]

expected: 0

case 4

in: [[4,8],[5,6]]

expected: 25

Starter codePython

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

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