mean square error loss in matlab

Here's the code to calculate mean square error (MSE) loss in Matlab for regression problems:

main.m
function mse = mean_squared_error(y_true, y_pred)
% Calculates the mean squared error between two arrays
% y_true: true values array
% y_pred: predicted values array
% returns: mean squared error value

    % calculate the difference between true and predicted values
    error = y_true - y_pred;
    
    % square the error values
    squared_error = error.^2;
    
    % calculate the mean of squared error values
    mse = mean(squared_error);
    
end
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To use this function, you need to pass two arrays y_true and y_pred, which contain the true and predicted values, respectively. The function calculates the error between them, squares the error values, and then takes the mean of the squared error values.

Here's an example of how to use this function:

main.m
% true and predicted values
y_true = [3, -0.5, 2, 7];
y_pred = [2.5, 0.0, 2, 8];

% calculate mean squared error
mse = mean_squared_error(y_true, y_pred);

% print the result
fprintf('Mean squared error: %f\n', mse);
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The output will be:

main.m
Mean squared error: 0.375000
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This means that the predicted values are, on average, 0.375 units away from the true values.

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