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Meta Decision Trees

http://ai.ijs.si/bernard/mdts/ 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. 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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Other links at Problem Solving > Decision Trees
Lecture about decision trees by Amos Storkey.
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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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Presents a worked example to explain the theory.
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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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Presents methods for analyzing decision trees, including exercises with solutions.
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