# Random Forest yields insanely OOB high error rates

I am using randomForestSRC to fit a random forest of 1000 trees for a 3-class classification problem with 160 cases. I am observing insanely high OOB error rates, sometimes as high as 75%, which is worse than guessing. What could be causing this? My calls are of the form

rfsrc(formula, data=df, block.size=1, ntree=1000, importance=TRUE,  verbose=FALSE, splitrule="auc", nsplit=NULL, nodesize=1)

• Your categories may be poorly described by your data, try a different algorithm. – user2974951 Aug 12 at 11:19