A Brief History of Decision Tree Implementation MAX AUSTIN - PowerPoint PPT Presentation

Mar 26, 2023 •24 likes •114 views

A Brief History of Decision Tree Implementation MAX AUSTIN Overview Famous Decision Tree Algorithms Chi-squared Automatic Interaction Detector (CHAID) Classification and Regression Tree (CART) Iterative Dichotomiser 3 (ID3)

A Brief History of Decision Tree Implementation MAX AUSTIN
Overview  Famous Decision Tree Algorithms  Chi-squared Automatic Interaction Detector (CHAID)  Classification and Regression Tree (CART)  Iterative Dichotomiser 3 (ID3)  C4.5  Personal Implementation
CHAID  Developed by Gordon V. Kass in 1980  Builds non-binary trees  Based on Bonferroni method  Allows multiple comparisons without a rise in Type I error  Used particularly for analysis of large data sets  i.e. marketing research
CART  Developed by Leo Breiman in 1984  Binary tree  Produces either classification trees or regression trees based on data  Classification trees predict the class or attribute of data  Regression trees predict the actual data value  Split using Gini Index  G = 1 – p 1 2 - p 2 2  G = 0 -> purity
ID3 and C4.5  Developed by John Ross Quinlan in 1986 and 1993  Uses entropy to split data sets  C4.5 implemented pruning and handles discrete and continuous data
Famous Decision Tree Implementation
Personal Implementation  Mainly based off of C4.5 algorithm  Does not prune tree  Handles specifically nominal data  Input files have possible attributes and features pre-defined
Basics of Algorithm  Determines best split by calculating entropy and information gain  Loops over all possible features for attribute  Recurse through tree until a pure feature is found or you run out of possible attributes  If no more attributes are available and there are multiple solutions possible, return the first one that occurs in the data
Results of Algorithm  Weather = 71.43%  Class = 60.00%  DeerHunter = 55.36%

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