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mva:mva [2016/08/07 10:31] – [Other information] iwn | mva:mva [2023/06/01 13:28] – [Tutorials and examples] 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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* [[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 a [[http://www.physik.uzh.ch/groups/ttp/members/grazzini/teaching/higgsnotes/lecture6.pdf|neural network]] and a [[http://www.physik.uzh.ch/groups/ttp/members/grazzini/teaching/higgsnotes/lecture10.pdf|BDT]] are visually explained in the UZH's [[http://www.physik.uzh.ch/groups/ttp/members/grazzini/teaching/higgs.html|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]] |
| * [[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> |