Decision tree learning is a predictive modeling approach that learns a tree structure from observations about examples. The learned branches encode tests or conditions, and the leaves represent conclusions about a target value or category.
Uses a decision tree (as a predictive model) to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining and machine learning.