Building Decision Tree on a high dimensional data set with sparse Boolean values 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? 
EDIT: 
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 
 A: Clarification of the problem
To clarify the OP's problem, as I read it (and your edit makes this more clear): Given a dataset with 10% events and 90% nonevents, a naive classifier could obtain 90% accuracy simply by choosing the 'nonevent' outcome every single iteration.
Tree-based algorithms (as well as other types of classifiers) can easily fall into this minimization 'mistake' because the algorithm is designed to treat all errors as equal; in many real-world situations, this is simply not acceptable, and one may prefer an algorithm that makes proportionally more false-alarm errors in order to improve the detection rate. 90% vs. 10% is actually a fairly benign example of this problem, in many real world cases the ratio is much worse.
Possible solutions
The two common ways to deal with this problem are:
1) Sampling. For each training step, either a) over-sample the underrepresented class, or b) under-sample the overrepresented class.
2) Error-weighting. Penalize misses of the event (model says "no event" when there is one) more than false alarms (model says "event" when there is no event).
Different packages that implement tree-based classification often implement one or both of these approaches out of the box.
