Current use of OOB prediction in pmm

[stephematician]

I was just having a cursory look at the missRanger algorithm, and I noticed this:

    data[v.na, v] <- if (pmm.k) pmm(xtrain = fit$predictions, 
                                    xtest = pred, 
                                    ytrain = data[[v]][!v.na], 
                                    k = pmm.k) else pred

The value of fit$predictions is the out-of-bag prediction - not the prediction of the forest. Is this the intended behaviour? The prediction of the random forest (in its entirety) can only be found via predict(fit, data=data[!v.na, union(v, completed))$predictions. Unfortunately, prediction is somewhat slow in ranger - if you have good reasons to use the out-of-bag predictions rather than the fitted forest predictions, then that'd be great.

[mayer79]

Thanks for looking into the code. It is indeed intended to use OOB predictions since insample predictions of random forests typically are badly overfitted and too close to the observed values:

library(ranger)

fit <- ranger(Sepal.Length ~ ., data = iris) # OOB R-squared: 0.8395822
insample_residuals <- iris$Sepal.Length - predict(fit, iris)$predictions
1 - var(insample_residuals) / var(iris$Sepal.Length) # Insample R-squared: 0.9546763