Decision Trees

I want to go through the Wikipedia series on Machine Learning and Data mining. Data mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.

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A decision tree is a decision support recursive partitioning structure that uses a tree-like model of decisions and their possible conseqeuences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control structures.

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.

A decision tree is a flowchart-like structure in which each internal node represents a test on an attribute, and each leaf node represents a class label. The paths from root to lead represent classification riles.

A decision tree consists of three types of nodes:

  1. Decision nodes- typically represented by squers
  2. Chance nodes - typically represented by circles
  3. End nodes - typically represented by triangles


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