btag:tmva
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| Both sides previous revisionPrevious revisionNext revision | Previous revisionNext revisionBoth sides next revision | ||
| btag:tmva [2014/12/17 14:43] – vlambert | btag:tmva [2014/12/17 14:49] – vlambert | ||
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| -**weight_norm** | -**weight_norm** | ||
| -**weight_flavour** : the ratio of the flavour prevalences in the evaluation process\\ | -**weight_flavour** : the ratio of the flavour prevalences in the evaluation process\\ | ||
| - | -**weight** | + | -**weight** |
| + | The training samples are now ready for the training process with **tmva_training.py**. Make sure to create a directory called "// | ||
| === Evaluation Samples === | === Evaluation Samples === | ||
| + | **1)** Make the trees really flat without vectors and set variables that are not defined for a given vertex category to a default value. For this, run your ntuples through **createNewTree.py** which will produce sets of new flat ntuples split in event range such as // | ||
| + | **2)** The evaluation trees can be skimmed as well to make the evaluation process faster. The script **skimTT.py** will reference the event ranges in the file names for the flat trees and copy new skimmed trees that contain 10% of the events from each of the flavour/ | ||
btag/tmva.txt · Last modified: 2014/12/17 14:53 by vlambert
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