I have a dataset of about ~49K entries and 31 columns. I ran a grid search with 3-fold CV for finding the hyperparameters of Random Forest (using sci-kit learn). I then used the best estimator to fit on the train set, and predict on the test set. The results of ROC AUC score are as follows:
CV: 0.705 Train: 0.836 Test: 0 .721
Can this be considered as overfitted? If so, what measures can I take to remedy this? So far, I have been spanning over n_estimators and max_depth. The model always seems to choose the maximum depth possible, and the difference between these scores increasing. I apply class weights to balance the dataset.