A decision tree is a hierarchical or tree like representation of decisions. Decision Tree is a technique to iteratively break input data (or node) into two or more data samples (or nodes). And this recursive partitioning of input data (or node) continue until it meets specified condition(s).
Decision Tree is a method for objective segmentation. Segmentation – A Perspective
The aim of decision tree based recursive partitioning data is to improve impurity measure of the output node(s). These nodes are called child node and the input node as parent node. If an algorithm break into two node at each stage, is called binary decision tree.
Example, banks and financial institutions grant credit facility after evaluating credit risk involved. Credit risk involved in credit decisions is evaluated using Credit Scorecard [Credit Score: What is it and how is it developed?]. Also, there are a few additional decisions involved in credit underwriting [Credit Underwriting: Minimize credit risk losses using Data Science and Analytics].
Read more @ Decision Tree: Statistical Tool
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