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The document discusses random forest methods for decision trees that use subsets of randomly selected attributes to generate trees and combine their predictions to improve accuracy over a single decision tree.

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Ahmed Tarek
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29 views1 page

00

The document discusses random forest methods for decision trees that use subsets of randomly selected attributes to generate trees and combine their predictions to improve accuracy over a single decision tree.

Uploaded by

Ahmed Tarek
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as DOCX, PDF, TXT or read online on Scribd
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Random forest method to decision trees derived from the information, and data, extracted

using the technology are displayed in a tree structure. Each root node of the tree, input
attribute, determination of each branch, and the node represents each result leaf’s result.
Random forest, using integrated technology, can improve the accuracy of the decision tree.
More specifically, the forest of the decision tree, each of the first is to use a subset of the
attributes that have been selected at random, will be generated. Then, to gather wood for it
to produce the most significant prediction.

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