# Understanding Decision Stump in Adaboost

I am trying to understand how Adaboost works, and many of the tutorials online involve the use of a decision tree stump. For example, http://www.cc.gatech.edu/~thad/6601-gradAI-fall2013/boosting.pdf or http://www.fromdev.com/2012/04/creating-weak-learner-with-decision.html. However, I am confused on how the plot is actually generated. Say I have the following training set:

               Event 1     Event 2     Event 3
Classifier 1      -1          1          1
Classifier 2      1           -1         1
Classifier 3      1           1          -1


How should I generate a decision stump for this?