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Decision Trees
Presents a worked example to explain the theory.
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This article explains how to use Decision Tree Analysis to choose between several courses of action.
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An applet which solves extensive form games (multi-player decision trees).
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Extensive form game solver. Part of Mike Shor's lecture notes for a course in Game Theory.
Interactive applets on Game Theory .net . Extensive Form Game Applet Solve sequential games and game trees This applet allows you to create extensive-form (sequential) games, and have them automatically solved for you. The applet allows up to four players, and up to 14 periods. To use the
Interactive applets on Game Theory .net . Extensive Form Game Applet Solve sequential games and game trees This applet allows you to create extensive-form (sequential) games, and have them automatically solved for you. The applet allows up to four players, and up to 14 periods. To use the
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ITI (Incremental Tree Inducer) - a program that constructs decision trees automatically from labeled examples.
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Machine Learning Laboratory
Incremental Decision Tree Induction
ITI (Incremental Tree Inducer) is a program that constructs decision trees automatically from
labeled examples. Although the program is called ITI, it includes
both the ITI and DMTI (Direct Metric Tree Induction algorithms.
One
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A Framework for Fast Decision Tree Construction of Large Datasets
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Data Mining Tools See5 and C5.0 - classifiers that are expressed as decision trees or sets of if-then rules.
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Data Mining Tools See5 and C5.0
Data mining is all about extracting patterns from an organization's
stored or warehoused data.
These patterns can be used to gain insight into aspects of the organization's
operations, and to predict outcomes for future situations as an
Patterns often concern the
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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.
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DTREG generates classification and regression trees.
DTREG is the ideal tool for modeling business and medical data with categorical variables such as sex, race and marital status. Decision trees present a clear, logical model that can be understood easily by people who are not mathematically inclined. If you have a need for linear or nonlinear
DTREG is the ideal tool for modeling business and medical data with categorical variables such as sex, race and marital status. Decision trees present a clear, logical model that can be understood easily by people who are not mathematically inclined. If you have a need for linear or nonlinear
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Presents methods for analyzing decision trees, including exercises with solutions.
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Decision Tree Primer This primer presents methods for analyzing decision trees, including exercises
with solutions. The material is in Adobe Portable Document Format (PDF). It
can be viewed or printed using Adobe Acrobat Reader, which is available free
. (Right-click on a link to download it
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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.
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Meta Decision Trees are a novel method for combining multiple classifiers. The difference
meta and ordinary decision trees (ODTs) is that MDT leaves specify which base-level
should be used, instead of predicting the class value directly (as ODTs). The attributes
MDTs are derived from the class
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SGI's MineSet Tree Visualizer for viewing decision-trees.
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The MLC++ decision-trees can be viewed using SGI's MineSet Tree Visualizer
The MLC++ decision-trees can be viewed using SGI's MineSet TM Tree Visualizer. The Tree Visualizer allows one to navigate (fly) through the tree, zoom in on interesting nodes, click on bars to get counts, and mark interesting places in the tree. Universities participating in SGI's varsity
The MLC++ decision-trees can be viewed using SGI's MineSet TM Tree Visualizer. The Tree Visualizer allows one to navigate (fly) through the tree, zoom in on interesting nodes, click on bars to get counts, and mark interesting places in the tree. Universities participating in SGI's varsity
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Chapter Six of Introduction to Machine Learning - Notes by Nils J. Nilsson.
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