A Decision Tree is a graphical representation of a procedure for classifying or evaluating an item or task. A decision tree represents a function that maps each element of its domain to an element of its range. Decision trees are often used as a method for making practical inductive inferences.
1.
Clustering vs. DecisionTrees
-
Comparison between the "bottom up" hierarchical clustering approach and "top down" splitting as used in decision tree segmentation.
2.
CRUISE Classification Tree
-
CRUISE is a statistical decision tree algorithm for classification. It is an improved descendant of an older algorithm called FACT.
Decision Tree Forests
-
DTREG generates a Decision Tree Forest that is an ensemble (collection) of decision trees whose predictions are combined to make the overall prediction for the forest.
6.
Decision Tree Primer
-
Presents methods for analyzing decision trees, including exercises with solutions.
7.
Decision Trees Tutorial
-
Tutorial Slides by Andrew Moore. A self-contained introduction to the flavor and terminology of data mining.
Incremental Decision Tree Induction
-
ITI (Incremental Tree Inducer) - a program that constructs decision trees automatically from labeled examples.
12.
Information on See5/C5.0
-
Data Mining Tools See5 and C5.0 - classifiers that are expressed as decision trees or sets of if-then rules.
13.
Meta Decision Trees
-
Meta Decision Trees are a novel method for combining multiple classifiers. MDT leaves specify which base-level classifier should be used, instead of predicting the class value directly.
Topics in Machine Learning: Decision Tree Learning
-
This webpage is intended to supplement Chapter 3: Decision Tree Learning of Tom Mitchell's Machine Learning. It follows the general structure of the chapter, but provides links to pages that elaborate on topics.
