Overfitting meaning your model is learning the noise from the data and its ability to generalize the results is very low. In this case you have a small training error but very large validation error. If you inspect (e.g. by plotting) the evolution of training and validation errors, you see that training error is always going down but validation error is goes up at some point. That is the point you need to stop training to avoid overfitting. I strongly recommend you to read this.
So, the 0.98 and 0.95 accuracy that you mentioned could be overfitting and could not! The point is that you also need to check the validation accuracy beside them. If validation accuracy is falling down then you are on overfitting zone!
What are some best practices in addition to a balanced input to avoid
It is called Prunning. Beside general ML strategies to avoid overfitting, for decision trees you can follow pruning idea which is described (more theoretically) here and (more practically) here. In SciKit-Learn, you need to take care of parameters like depth of the tree or maximum number of leafs.