Here's the code to calculate mean square error (MSE) loss in Matlab for regression problems:
main.m456 chars17 lines
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.m217 chars10 lines
The output will be:
main.m29 chars2 lines
This means that the predicted values are, on average, 0.375 units away from the true values.
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