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
	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
	This tutorial shows how the content of an RDataFrame can be converted to an RTensor object.
file	tmva003_RReader.C
	This tutorial shows how to apply with the modern interfaces models saved in TMVA XML files.
file	tmva004_RStandardScaler.C
	This tutorial illustrates the usage of the standard scaler as preprocessing method.
file	tmva100_DataPreparation.py
	This tutorial illustrates how to prepare ROOT datasets to be nicely readable by most machine learning methods.
file	tmva101_Training.py
	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
	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
	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
	TMVA Classification Example Using a Convolutional Neural Network
file	TMVA_Higgs_Classification.C
	Classification example of TMVA based on public Higgs UCI dataset
file	TMVA_RNN_Classification.C
	TMVA Classification Example Using a Recurrent Neural Network
file	TMVA_SOFIE_Keras.C
	This macro provides a simple example for the parsing of Keras .h5 file into RModel object and further generating the .hxx header files for inference.
file	TMVA_SOFIE_ONNX.C
	This macro provides a simple example for the parsing of ONNX files into RModel object and further generating the .hxx header files for inference.
file	TMVA_SOFIE_PyTorch.C
	This macro provides a simple example for the parsing of PyTorch .pt file into RModel object and further generating the .hxx header files for inference.
file	TMVAClassification.C
	This macro provides examples for the training and testing of the TMVA classifiers.
file	TMVAClassificationApplication.C
	This macro provides a simple example on how to use the trained classifiers within an analysis module
file	TMVAClassificationCategory.C
	This macro provides examples for the training and testing of the TMVA classifiers in categorisation mode.
file	TMVAClassificationCategoryApplication.C
	This macro provides a simple example on how to use the trained classifiers (with categories) within an analysis module
file	TMVACrossValidation.C
	This macro provides an example of how to use TMVA for k-folds cross evaluation.
file	TMVACrossValidationApplication.C
	This macro provides an example of how to use TMVA for k-folds cross evaluation in application.
file	TMVACrossValidationRegression.C
	This macro provides an example of how to use TMVA for k-folds cross evaluation.
file	TMVAGAexample.C
	This executable gives an example of a very simple use of the genetic algorithm of TMVA
file	TMVAGAexample2.C
	This executable gives an example of a very simple use of the genetic algorithm of TMVA.
file	TMVAMinimalClassification.C
	Minimal self-contained example for setting up TMVA with binary classification.
file	TMVAMulticlass.C
	This macro provides a simple example for the training and testing of the TMVA multiclass classification
file	TMVAMulticlassApplication.C
	This macro provides a simple example on how to use the trained multiclass classifiers within an analysis module
file	TMVAMultipleBackgroundExample.C
	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 algorithm.
file	TMVARegression.C
	This macro provides examples for the training and testing of the TMVA classifiers.
file	TMVARegressionApplication.C
	This macro provides a simple example on how to use the trained regression MVAs within an analysis module