How to determine if my GBM model is overfitting?
The term overfitting means the model is learning relationships between attributes that only exist in this specific dataset and do not generalize to new, unseen data.
Just by looking at the model accuracy on the data that was used to train the model, you won't be able to detect if your model is or isn't overfitting. 

To see if you are overfitting, split your dataset into two separate sets:


a train set (used to train the model)
a test set (used to test the model accuracy) 


A 90% train, 10% test split is very common. Train your model on the train test and evaluate its performance both on the test and the train set. If the accuracy on the test set is much lower than the models accuracy on the train set, the model is overfitting. 

You can also use cross-validation (e.g. splitting the data into 10 sets of equal size, for each iteration use one as test and the others as train) to get a result that is less influenced by irregularities in your splits.