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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.
> Decision Tree Analysis The Mind Tools Career Excellence Club gives you the training, coaching and support you need to make a lasting success of your career. Take our FREE tour, and find out
> Decision Tree Analysis The Mind Tools Career Excellence Club gives you the training, coaching and support you need to make a lasting success of your career. Take our FREE tour, and find out
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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
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
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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
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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
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
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Presents methods for analyzing decision trees, including exercises with solutions.
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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
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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
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
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Chapter Six of Introduction to Machine Learning - Notes by Nils J. Nilsson.
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CRUISE is a statistical decision tree algorithm for classification. It is an improved descendant of an older algorithm called FACT.
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CRUISE Classification Tree (version 3.6.3)
© HyunJoong Kim 1997-2008
CRUISE is a statistical decision tree algorithm for classification
(also called supervised learning) developed by Hyunjoong
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