| Both sides previous revisionPrevious revisionNext revision | Previous revisionNext revisionBoth sides next revision |
| mva:mva [2016/08/07 10:20] – [Other information] iwn | mva:mva [2023/06/01 13:14] – [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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| ===== 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 [[|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> |
