I am training a model using random forest in Python. The dataset is about 5 million entries with ~60 numerical features, and my target is to do a classification among 30 different groups.
When I apply the model to the data I used for training, it gives almost perfect prediction. That makes me feel like I am overfitting this.
In general, what is supposed to happen if one trains a random forest model with a dataset, then make predictions on the exactly same data? What are good and bad signs, and what shall one consider doing in any bad scenario?