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| mva:mva [2016/08/06 13:52] – 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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| </code> | </code> |
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| In the empty quote marks you can add cuts or options. More information on can be found on the [[https://root.cern.ch/doc/v606/classTMVA_1_1Factory.html|Factory Class Reference]]. | In the empty quote marks you can add cuts or options. More information on can be found in section 3.1 in the [[http://tmva.sourceforge.net/docu/TMVAUsersGuide.pdf|manual]] and in the [[https://root.cern.ch/doc/v606/classTMVA_1_1Factory.html|Factory Class Reference]]. |
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| Then you can book multiple methods like the BDT and MLP for different parameters: | Then you can book multiple methods like the BDT and MLP for different parameters: |
| Finally train, test and evaluate all the booked methods: | Finally train, test and evaluate all the booked methods: |
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| <code> | <code python> |
| factory.TrainAllMethods() | factory.TrainAllMethods() |
| factory.TestAllMethods() | factory.TestAllMethods() |
| f_out.Close() | f_out.Close() |
| </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> |
