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mva:mva [2016/08/07 10:18] – [Training] iwn | mva:mva [2023/06/01 13:29] (current) – [Other information] iwn |
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* [[https://root.cern.ch/doc/v606/classTMVA_1_1Factory.html|Factory Class Reference]] | * [[https://root.cern.ch/doc/v606/classTMVA_1_1Factory.html|Factory Class Reference]] |
* [[https://root.cern.ch/doc/v606/classTMVA_1_1Reader.html|Reader Class Reference]] | * [[https://root.cern.ch/doc/v606/classTMVA_1_1Reader.html|Reader Class Reference]] |
| * [[https://root.cern.ch/doc/master/group__tutorial__tmva.html|Official examples (C++)]] |
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When using the neural netwerk method MLP, you might need ROOT 5.34.0.0 or newer, to have larger buffer for the xml reader, for example: | When using the neural netwerk method MLP, you might need ROOT 5.34.0.0 or newer, to have larger buffer for the xml reader, for example: |
. /afs/cern.ch/sw/lcg/app/releases/ROOT/5.34.26/x86_64-slc6-gcc48-opt/root/bin/thisroot.sh | . /afs/cern.ch/sw/lcg/app/releases/ROOT/5.34.26/x86_64-slc6-gcc48-opt/root/bin/thisroot.sh |
</code> | </code> |
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* ''MaxDepth=3'': maximum tree depth, ~2-5 depending on interaction of the variables | * ''MaxDepth=3'': maximum tree depth, ~2-5 depending on interaction of the variables |
* ''nCuts=20'': grid points in variable range to find the optimal cut in node splitting | * ''nCuts=20'': grid points in variable range to find the optimal cut in node splitting |
* ''SeparationType=GiniIndex'': separating criterion at each splitting node to select best variable. The [[https://en.wikipedia.org/wiki/Gini_coefficient|Gini index]] is one measure. | * ''SeparationType=GiniIndex'': separating criterion at each splitting node to select best variable. The [[https://en.wikipedia.org/wiki/Gini_coefficient|Gini index]] is one often used measure |
* ''MinNodeSize=5%'': minimum percentage of training events required in a leaf node | * ''MinNodeSize=5%'': minimum percentage of training events required in a leaf node |
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* [[https://aholzner.wordpress.com/2011/08/27/a-tmva-example-in-pyroot/|TMVA in PyRoot tutorial]] | * [[https://aholzner.wordpress.com/2011/08/27/a-tmva-example-in-pyroot/|TMVA in PyRoot tutorial]] |
* [[mva:mvaexample|This python example]] shows how you can run over multiple trees and different sets of variables. It also includes functions to apply the MVA output to trees and make background rejection vs. signal efficiency plots and correlation plots. | * [[mva:mvaexample|This python example]] shows how you can run over multiple trees and different sets of variables. It also includes functions to apply the MVA output to trees and make background rejection vs. signal efficiency plots and correlation plots. |
| * [[https://root.cern/doc/master/group__tutorial__tmva.html|ROOT official TMVA tutorials]] |
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===== Other information ===== | ===== Other information ===== |
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* The working principles of [[http://www.physik.uzh.ch/groups/ttp/members/grazzini/teaching/higgsnotes/lecture6.pdf|neural network]] and [[http://www.physik.uzh.ch/groups/ttp/members/grazzini/teaching/higgsnotes/lecture10.pdf|BDT]] is visually explained in the UZH's [[|Higgs Physics course]] | * The working principles of a [[https://www.physik.uzh.ch/~grazzini/teaching/higgsnotes/lecture6.pdf|neural network]] and a [[https://www.physik.uzh.ch/~grazzini/teaching/higgsnotes/lecture10.pdf|BDT]] are visually explained in the UZH's [[https://www.physik.uzh.ch/~grazzini/teaching/higgs.html|Higgs Physics course]] by Mauro Donegà. |
| * [[https://arogozhnikov.github.io/2016/07/05/gradient_boosting_playground.html|Gradient Boosting Interactive Playground]] with interactive visuals |
* TMVA BibTex reference: | * TMVA BibTex reference: |
<code latex> | <code latex> |