I'm using a Random Forest algorithm in order to construct a classification model, and I HAVE to check the accuracy of my rf model in the training sample, but as you can see in this answers : https://stats.stackexchange.com/a/112052/90446https://stats.stackexchange.com/a/66546/90446you can't evaluate the accuracy considering the training samples like this:predict(model, data=train)I'm not confortable with the idea of use OOB to get accuracy of the training sample, because the OOB was not used to build the model, how could this be right? I don't know what should I do to predict the fit in the training sample and get the accuracy of the training sample, is it possible or make any sense? When a check the AUC of the prediction of my training sample I get something near of 0.98, but the AUC of the test sample is about 0.7. Is this  due to the limitations of prediction at the training sample or due to Overfitting? 
Prediction in the training sample with randomforest in r
machine learning;r;predictive modeling;random forest;accuracy