In this chapter, the authors show how to build a decision tree from given real-time data. They interpret the output of decision tree by learning decision tree classifier using really recursive greedy algorithm. Feature selection is made based on classification error using the algorithm called feature split selection algorithm (FSSA), with all different possible stopping conditions for splitting. The authors perform prediction with decision trees using decision tree prediction algorithm (DTPA), followed by multiclass predictions and their probabilities. Finally, they perform splitting procedure on real continuous value input using threshold split selection algorithm (TSSA).