Mean Squared Error Loss

Problem

Compute mean squared error between predictions and targets.

L = (1/n) * sum((y_pred_i - y_true_i)^2). Return 0.0 for empty inputs.

Hint

Square the residuals, average, and the loss stays non-negative.

case 1

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

expected: 0

case 2

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

expected: 2.5

case 3

in: [[0],[0]]

expected: 0

case 4

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

expected: 2.6666666666666665

Starter codePython

def mse_loss(y_pred, y_true):
    # Your code here
    pass

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