In my understanding random forest model will keep one third of the data for testing the model. That means we do not need to explicitly test the model with new data.
Assume we build a random forest model with training data. After that we receive a new set of samples for each month.
Do we need to use the existing model for predicting the new set of samples or do we need to build an entirely new model with these samples?