# 2.6.1: Supplmental - Decision Tables/Trees

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## Decision Tables

Decision tables are a concise visual representation for specifying which actions to perform depending on given conditions. They are algorithms whose output is a set of actions. The information expressed in decision tables could also be represented as decision trees or in a programming language as a series of if-then-else and switch-case statements.

The limited-entry decision table is the simplest to describe. The condition alternatives are simple Boolean values, and the action entries are check-marks, representing which of the actions in a given column are to be performed.

Example: A person seeking admission to PGCC is required to:

· Possess a high school diploma or GED

· Fill out an admission form

 Requirements R1 R2 R3 R4 R5 R6 R7 R8 Conditions High School Diplomat N N N N Y Y Y Y GED N N N Y Y N Y Y Fill Out Admission Form N N Y Y Y N N Y Admission fee N Y Y Y Y N N N Actions Accepted X X Rejected X X X X X X

Of course, this is just a simple example, but even so, it demonstrates how decision tables can scale to several conditions with many possibilities.

## Decision Tree

A decision tree is a decision support tool 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.

## Decision tree elements

Drawn from left to right, a decision tree has only burst nodes (splitting paths) but no sink nodes (converging paths). Therefore, used manually, they can grow very big and are then often hard to draw fully by hand. Traditionally, decision trees have been created manually – as the aside example shows – although increasingly, specialized software is employed.

## Decision rules

The decision tree can be linearized into decision rules,[2] where the outcome is the contents of the leaf node, and the conditions along the path form a conjunction in the if clause. In general, the rules have the form:

if condition1 and condition2 and condition3 then outcome.

Decision rules can be generated by constructing association rules with the target variable on the right. They can also denote temporal or causal relations.[3]

2.6.1: Supplmental - Decision Tables/Trees is shared under a not declared license and was authored, remixed, and/or curated by LibreTexts.