I would like to use the OOB cases from a random forest fit to estimate the mean squared prediction error so I don't have to cross-validate. I am using the randomForest package in R. It is clear from the documentation that OOB error is reported for classification, but I can't figure out how to get OOB MSPE for regression. Am I missing it or is it truly not reported, which seems odd?

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#### Best Answer

It's not reported.

But you can use the `predicted`

part of the fit, which as per the help page yields

the predicted values of the input data based on out-of-bag samples.

An example:

`set.seed(131) ozone.rf <- randomForest(Ozone ~ ., data=airquality,na.action=na.omit) mean((ozone.rf$predicted-airquality$Ozone[as.numeric(names(ozone.rf$predicted))])^2) [1] 316.7915 `

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