Root Mean Squared Error of Forecasts

Problem

Compute the root mean squared error between actual values and forecasts: sqrt((1/n) * sum((actual[t] - forecast[t])^2)).

Return 0.0 when the actual list is empty.

Hint

Square the errors, average, then take the square root. Large errors dominate.

case 1

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

expected: 1.9148542155126762

case 2

in: [[0,0],[3,4]]

expected: 3.5355339059327378

case 3

in: [[1,2],[1,2]]

expected: 0

Starter codePython

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

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