Here’s how to calculate the root mean square error.
Assume you have one set of numbers that represent the Actual values you want to predict.
Actual = [1 2 3 4];
Then assume you have another set of numbers that Predicted the actual values.
Predicted = [1 3 1 4];
How do you evaluate how close Predicted values are to the Actual values?
Well you could use the root mean square error (RMSE) to give a sense of the Predicted values error.
Here’s some MATLAB code that does exactly that.
% rmse tutorial. % The actual values that we want to predict. Actual = [1 2 3 4]; % The values we actually predicted. Predicted = [1 3 1 4]; % One way is to use the Root Mean Square function and pass in the "error" part. rmse = rms(Predicted-Actual) % That's it! You're done. % But for those of you who are the curious type, % here's how to calculate the root-mean-square-error by hand. % First calculate the "error". err = Actual - Predicted; % Then "square" the "error". squareError = err.^2; % Then take the "mean" of the "square-error". meanSquareError = mean(squareError); % Then take the "root" of the "mean-square-error" to get % the root-mean-square-error! rootMeanSquareError = sqrt(meanSquareError) % That's it! You have calculated the RMSE by hand. % So, this is true. rootMeanSquareError == rmse