Re: [R] randomForest() for regression produces offset predictions

I would expect this regression towards the mean behavior on a new or hold out dataset, not on the training data. In RF terminology, this means that the model prediction from predict is the in-bag estimate, but the out-of-bag estimate is what you want for prediction. In Joshua's example, rf.rf$pred

Re: [R] randomForest() for regression produces offset predictions

What I see is the predictions being less extreme than the actual values -- predictions for large actual values are smaller than the actual, and predictions for small actual values are larger than the actual. That makes sense to me. The object is to maximize out-of-sample predictive power, not in-

[R] randomForest() for regression produces offset predictions

Hi all, I have observed that when using the randomForest package to do regression, the predicted values of the dependent variable given by a trained forest are not centred and have the wrong slope when plotted against the true values. This means that the R^2 value obtained by squaring the Pea