# Matlab RandomForest prediction error calculation

I am using Random Forests in Matlab for regression. After educating my model on train data, I want to get MSE on test data not used in training. I do that two ways:

1. call predict and directly calculate MSE using predicted and actual values
2. call error and use built in TreeBagger functionality to do the same task.

In first case I get 10 times bigger result. Why? The only explanation I have, is that built in function somehow discounts outliers in prediction, but I am not sure how exactly it is done.

Can somebody, please, explain all this to me.

However, one of the big advantages of bagging is that it can produce an approximation of error on train set as it would be a test -- this is called out-of-bag (OOB) and those predictions (available in OOBPred) are used by Matlab in error to produce "true" error.