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


main.m
function out = mse_loss(x, y)
% This function computes the mean squared error (MSE) loss between the
% predicted values and true labels.

% Check if the input vectors are of the same length
if length(x) ~= length(y)
    error('Input vectors have different length!')
end 

% Compute the MSE loss using the formula: mse = mean((x - y).^2)
out = mean((x - y).^2);

end
366 chars
14 lines

The above function first checks if the two input vectors are of the same length. If they are not, it returns an error. Otherwise, it computes the MSE loss using the formula mse = mean((x - y).^2), where x is the predicted vector and y is the true label vector. The output out is the calculated MSE loss.

similar matlab code snippets

mean square error loss in matlab

empirical detection-error loss in matlab

design the function function loss = loss_func(t_hat, t) that computes the empirical loss f binary predictions t_hat in matlab

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

detection loss in matlab

design the function function loss = detection_error_loss(t_hat, t) that computes the empirical detection-error loss of binary predictions t_hat ith respect to the true targets t in matlab

quadratic loss by solving a standard least squares problem over training data matrix in matlab

design the function function out = mse_loss(t_hat, t) that computes the empirical mean square error loss of prediction t_hat in matlab

function loss in matlab

error loss in matlab

related categories