I have been trying to learn a decision tree on a data-set with almost 400 features. The target variable has only two values and the data is highly skewed towards the non-event class (90 % of the data set).
All the features take only Boolean values. The decision tree plots are skewed towards the non event class. I am looking for the path that helps me predict the event class. I couldn't find a definitive answer anywhere. Can anyone give me some ideas?
Apologies if the question wasn't clear. I just started as a Data Science inter, and this is my first project.
The decision tree predicts almost all the non-event class (90% of the data) with high accuracy, whereas almost entirely misclassifies the event class.
What do I need to do to build the tree that predicts the non-event class with high accuracy and to see the decision paths that makes up such a tree?
I tried ensemble methods but it only improved my classifier marginally