RMSE in MATLAB

Root Mean Square Error (RMSE) is a commonly used metric to evaluate the performance of regression models. It measures the difference between predicted values and actual values of a model. In MATLAB, calculating RMSE is easy and can be done using built-in functions.

Function Syntax

The syntax for calculating RMSE in MATLAB is as follows:

rmse = sqrt(mean((predicted - actual).^2))

where:

• predicted: vector or matrix containing predicted values
• actual: vector or matrix containing actual values
• rmse: scalar value of the root mean square error

Example

Here's an example of how to calculate RMSE in MATLAB:

% Generate random data
predicted = rand(1,10);
actual = rand(1,10);

% Calculate RMSE
rmse = sqrt(mean((predicted - actual).^2));
fprintf('RMSE: %f\n', rmse);

Output:

RMSE: 0.296098

Conclusion

In conclusion, RMSE is a useful metric to evaluate the accuracy of regression models. In MATLAB, it can be easily calculated using the sqrt and mean functions.