Colleagues of mine recently presented a work where they calibrate boosted regression trees (BRT) models on small data sets ($n= 30$). They validated the models using leave-one-out cross validation (LOOCV) using R2, RMSPE and RPD indices. They also provided these indices computed by training and validating the model on the full dataset. The R2, RMSPE and RPD values obtained through LOOCV were almost strictly equal to the R2, RMSPE and RPD values obtained when validating on the training data set.
My questions are :
Is such a results expected for LOOCV on BRT?
Is this because BRT is relatively insensitive to outliers (and to single individuals?) that excluding one individual during LOOCV does not make a difference, providing nearly similar calibrated models with same performance metrics on the excluded individuals?
In that case does LOOCV for BRT makes any sense, compared to repeated k-fold CV with $k < n$?
Thank you in advance