Consider I have one dependent variable to predict 'Attitude' which can take three values 'Positive/Negative/Neutral'. I have following independent variables or features- Age, Height, Gender, Income etc. I trying to predict Attitude using decision tree classifier.
Attitude ~ Age + Height + Gender + Income (Decision Tree)
I am getting >90% accuracy for the when tree depth is 15. As tree is dividing on continuous variables (i.e. Age, Income and Height) again and again to get leaf with pure classes.
Is this problem of overfitting? Should I convert the continuous variables into categorical variables (like range classes)?