I am working on classification problem where I need to categorize the user in buy/ non-buy category. I have around 100 + features or predictors to predict the behavior of user.
I tried to implement with Random forest and Gradient Boosting to get better prediction compare to decision tree. I am getting better performance when I evaluate against performance parameters like roc_auc,accuracy, precision and recall when using ensemble techniques.
I also extracted important features that are responsible for my predictions but I am not able to visualize the model fully. Some how random forest works as black box where i am not getting what is the contribution of each tree, which features are been considered in each trees, etc.
Is there any way through which I can find out more information from Random Forest Model?