design the function function out = mse_loss(t_hat, t) that computes the empirical mse loss of prediction t_hat using the true labels t, both vectors of the same length. in matlab

main.m
function out = mse_loss(t_hat, t)
% Computes the empirical mean squared error loss of prediction t_hat using
% the true labels t, both vectors of the same length.
%
% Input:
% t_hat: predicted labels (Nx1 vector)
% t: true labels (Nx1 vector)
%
% Output:
% out: empirical mean squared error loss (scalar)

N = length(t);
out = sum((t_hat - t).^2) / N;
end
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Explanation:

The function mse_loss takes two input arguments, t_hat and t, which represent the predicted labels and true labels, respectively. The function returns the empirical mean squared error loss of t_hat compared to t. The length of the two vectors is assumed to be the same.

To compute the mean squared error loss, we take the difference between each predicted label and its corresponding true label, square it, and then take the average of these values. The result is a scalar value representing the mean square error loss between the predicted and true labels. This value is returned as out.