A Decision Tree helps us to make decisions by mapping out different choices and their possible outcomes. It’s used in machine learning for tasks like classification and prediction. In this article, we’ll see more about Decision Trees, their types and other core concepts. Learn what a decision tree is, how it works, and why it is useful for machine learning. This article covers the basic terminology, the algorithm steps, and the attribute selection measures of decision trees. Learn how to use decision tree algorithm to classify or predict outcomes based on a set of rules. See examples, types, implementation, and assumptions of the algorithm with Python code and visualization. A decision tree is a supervised learning algorithm used for both classification and regression tasks. It has a hierarchical tree structure which consists of a root node, branches, internal nodes and leaf nodes.
