Go to TogaWare.com Home Page. Data Science Desktop Survival Guide
by Graham Williams
Duck Duck Go

Chapter: Decision Trees

20200815 Decision trees are widely used in data mining and well supported in R (R Core Team, 2020). Decision tree learning deploys a divide and conquer approach, known as recursive partitioning. It is usually implemented as a greedy search using information gain or the Gini index to select the best input variable on which to partition the dataset at each step.

Here we introduce rattle and rpart for building decision trees. We begin with a step-by-step example of building a decision tree using Rattle, and then illustrate the process using R begining with Section [*]. We cover both classification trees and regression trees.

We have briefly introduced decision trees as an algorithm in Section 16.3 and Chapter 12 uses decision trees as the model builder to demonstrate the model template. Examples of decision tree induction are available through the rain, iris, and pyiris packages from MLHub.


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Other online resources include the GNU/Linux Desktop Survival Guide.
Books available on Amazon include Data Mining with Rattle and Essentials of Data Science.
Popular open source software includes rattle and wajig.
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