I'm using randomforest regressor to predict values. I'm trying to make a automatic learning model. My database has let's say 100 rows. So, I train my model with only 10 rows. And one by one I want to predict a value and see if with only 10 rows, my random Forest can predict the value correctly.
If not, then I will insert this new row in the trainset (with 10 values, now becoming 11) then try again with the next value and so forth until 100.
Is there a way to find out, before predicting the value with the model, if randomForest can precisely predict the value? Would an outlier detector be useful?
I am looking for something that will say : Oh I have never seen a value like this, I can't predict it precisely.
I tried using the lofactor function from DMwR package but the outlier method just finds the outlier of the whole TrainSet and not wether or not the last value I added in the Trainset is an outlier or not.
I hope this is somewhat clear, thank you all