ROOT 6.12/07 Reference Guide |
Directories | |
directory | envelope |
directory | keras |
Files | |
file | createData.C [code] |
Plot the variables. | |
file | TMVAClassification.C [code] |
This macro provides examples for the training and testing of the TMVA classifiers. | |
file | TMVAClassificationApplication.C [code] |
This macro provides a simple example on how to use the trained classifiers within an analysis module | |
file | TMVAClassificationCategory.C [code] |
This macro provides examples for the training and testing of the TMVA classifiers in categorisation mode. | |
file | TMVAClassificationCategoryApplication.C [code] |
This macro provides a simple example on how to use the trained classifiers (with categories) within an analysis module | |
file | TMVACrossValidation.C [code] |
This example explains how to use the cross-validation feature of TMVA. | |
file | TMVAGAexample.C [code] |
This exectutable gives an example of a very simple use of the genetic algorithm of TMVA | |
file | TMVAGAexample2.C [code] |
This exectutable gives an example of a very simple use of the genetic algorithm of TMVA. | |
file | TMVAMulticlass.C [code] |
This macro provides a simple example for the training and testing of the TMVA multiclass classification | |
file | TMVAMulticlassApplication.C [code] |
This macro provides a simple example on how to use the trained multiclass classifiers within an analysis module | |
file | TMVAMultipleBackgroundExample.C [code] |
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 [code] |
This macro provides examples for the training and testing of the TMVA classifiers. | |
file | TMVARegressionApplication.C [code] |
This macro provides a simple example on how to use the trained regression MVAs within an analysis module | |