Decision Tree on Unbalanced Data While running decision tree, I have unbalanced data. The data balance is 93%(Class 0) to 7% (Class 1).
Now when I am plotting decision tree to understand the factors contributing to class 1, then I find that most boxes are for 0 class (as it is 97% of the data).
Also, after pruning, very few contributors are there which are contributing to class 1. How can I get the factors contributing to class 1?
Which function are you using at Loss? Using the right one is important when dealing with imbalanced datasets. 7% is imbalanced, but not that bad. 

Have you tried any eXplainable Artificial Intelligence (XAI) method? Normally I use Shap.
It is really good to see which feature contributes in which direction. You can see an example here.



You can check the github here
You can check the paper here