I was provided with quite a small sample of labeled (variable of interest) observations to train a model to predict unlabeled observations. All the observations are associated with many covariates. I'm assuming that the trained model will do better in measure of how well does this small labeled sample represent the unlabeled cases. Using only the covariates is there any way to measure if an unlabeled case will be poorly predicted or not? I can imagine that if the covariates are standardized and you measure the euclidean distance (covariates are continuous) from a unlabeled point to the labeled ones and this unlabeled one tends to be "far away" from the labeled sample the prediction accuracy would drop. I'm not really sure. Does anyone have any comments on this or any pointers on what to read to assess this or if my ideas are just totally off?
By the way, the techniques I tried out for this task are Random Forests, MARS and boosted regression trees.