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Reference Guide
 
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TMVA tutorials

Example code which illustrates how to use the TMVA toolkit.

Modules

 Envelope Tutorials
 
 TMVA Keras tutorials
 Example code which illustrates how to use keras with the python interface of TMVA.
 
 TMVA PyTorch tutorials
 Example code which illustrates how to use pytorch with the python interface of TMVA.
 

Files

file  createData.C
 Plot the variables.
 
file  tmva001_RTensor.C
  View in nbviewer Open in SWAN This tutorial illustrates the basic features of the RTensor class, RTensor is a std::vector-like container with additional shape information.
 
file  tmva002_RDataFrameAsTensor.C
  View in nbviewer Open in SWAN This tutorial shows how the content of an RDataFrame can be converted to an RTensor object.
 
file  tmva003_RReader.C
  View in nbviewer Open in SWAN This tutorial shows how to apply with the modern interfaces models saved in TMVA XML files.
 
file  tmva004_RStandardScaler.C
  View in nbviewer Open in SWAN This tutorial illustrates the usage of the standard scaler as preprocessing method.
 
file  tmva100_DataPreparation.py
  View in nbviewer Open in SWAN This tutorial illustrates how to prepare ROOT datasets to be nicely readable by most machine learning methods.
 
file  tmva101_Training.py
  View in nbviewer Open in SWAN This tutorial show how you can train a machine learning model with any package reading the training data directly from ROOT files.
 
file  tmva102_Testing.py
  View in nbviewer Open in SWAN This tutorial illustrates how you can test a trained BDT model using the fast tree inference engine offered by TMVA and external tools such as scikit-learn.
 
file  tmva103_Application.C
  View in nbviewer Open in SWAN This tutorial illustrates how you can conveniently apply BDTs in C++ using the fast tree inference engine offered by TMVA.
 
file  TMVA_CNN_Classification.C
  View in nbviewer Open in SWAN TMVA Classification Example Using a Convolutional Neural Network
 
file  TMVA_Higgs_Classification.C
  View in nbviewer Open in SWAN Classification example of TMVA based on public Higgs UCI dataset
 
file  TMVA_RNN_Classification.C
  View in nbviewer Open in SWAN TMVA Classification Example Using a Recurrent Neural Network
 
file  TMVAClassification.C
  View in nbviewer Open in SWAN This macro provides examples for the training and testing of the TMVA classifiers.
 
file  TMVAClassificationApplication.C
  View in nbviewer Open in SWAN This macro provides a simple example on how to use the trained classifiers within an analysis module
 
file  TMVAClassificationCategory.C
  View in nbviewer Open in SWAN This macro provides examples for the training and testing of the TMVA classifiers in categorisation mode.
 
file  TMVAClassificationCategoryApplication.C
  View in nbviewer Open in SWAN This macro provides a simple example on how to use the trained classifiers (with categories) within an analysis module
 
file  TMVACrossValidation.C
  View in nbviewer Open in SWAN This macro provides an example of how to use TMVA for k-folds cross evaluation.
 
file  TMVACrossValidationApplication.C
  View in nbviewer Open in SWAN This macro provides an example of how to use TMVA for k-folds cross evaluation in application.
 
file  TMVACrossValidationRegression.C
  View in nbviewer Open in SWAN This macro provides an example of how to use TMVA for k-folds cross evaluation.
 
file  TMVAGAexample.C
  View in nbviewer Open in SWAN This exectutable gives an example of a very simple use of the genetic algorithm of TMVA
 
file  TMVAGAexample2.C
  View in nbviewer Open in SWAN This exectutable gives an example of a very simple use of the genetic algorithm of TMVA.
 
file  TMVAMinimalClassification.C
  View in nbviewer Open in SWAN Minimal self-contained example for setting up TMVA with binary classification.
 
file  TMVAMulticlass.C
  View in nbviewer Open in SWAN This macro provides a simple example for the training and testing of the TMVA multiclass classification
 
file  TMVAMulticlassApplication.C
  View in nbviewer Open in SWAN This macro provides a simple example on how to use the trained multiclass classifiers within an analysis module
 
file  TMVAMultipleBackgroundExample.C
  View in nbviewer Open in SWAN This example shows the training of signal with three different backgrounds Then in the application a tree is created with all signal and background events where the true class ID and the three classifier outputs are added finally with the application tree, the significance is maximized with the help of the TMVA genetic algrorithm.
 
file  TMVARegression.C
  View in nbviewer Open in SWAN This macro provides examples for the training and testing of the TMVA classifiers.
 
file  TMVARegressionApplication.C
  View in nbviewer Open in SWAN This macro provides a simple example on how to use the trained regression MVAs within an analysis module