Decision Tree Analysis | 1031 Portal

Decision tree analysis is a decision support recursive partitioning structure that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements. Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal, but are also a popular tool in machine learning. The concept of decision trees has been around for decades. Decision trees can handle both categorical and numerical data, and are particularly useful when the data is complex and nonlinear. Decision trees can be used for both supervised and unsupervised learning tasks.