TMVA Package

Factory

• NormMode: as called in PrepareTrainingAndTestTree
  The default has changed and is now "EqualNumEvents" (with fixed meaning) While previously NumEvents and EqualNumEvents (by mistake/miscommunication) took into account training+test events, they are now correctly normalising only "Training Events" (note the reason for these normalisations was to have the possibility to easily force the effective (weighted) number of training events used for Signal (class 0) to equal the number of training events in the Backgr. (sum of all remaining classes in multiclass mode)

  NumEvents:

  - the weighted number of events is scaled, independently for signal and backgroundm, such that the sum of weights equals the number of events given in the Factory::PrepareTrainingAndTestTree("",nTrain_Signal=3000,nTrain_Background=6000) call. This example call will give hence end up in having 2x more background events in the training compared to the signal, no matter what the individual event weights have been. (watch out! if you specify nTrain_Signal=0,nTrain_Background=0), then the ratio will be according to total numbers of MC events in the signal and background respectively, which could be very different from the usually good ratio of having about the same weighted number signal and background events in the training. In that case it is better to use:

  EqualNumEvents:

  - for the signal events, the same is done as for NumEvents. The background events however are reweighted such, that their sum of weights equals that for the signal events. This results in the same effective (weighted) number of signal and background events to be seen in the training.

• Transformations=I is default again in Factory (this defines which variables distribution plots are added to the TMVA output file - and hence displayable via the TMVAGui)

Boosted Decision Trees

nEventsMin (deprecated) please replace by --> MinNodeSize

The option nEventsMin which specified the minimum number of training event in a leaf node as an absolute number has been replaced by "MinNodeSize" which is given in "percentage of the trainin sample". Like this the training options become less dependent on the actual number of training sample size

NNodesMax (deprecated) please replace by --> MaxDepth

GradBaggingFraction and UseNTrainEvents replaced by BaggedSampleFraction

- they both meant the same thing and are now deprecated --> use BaggedSampleFraction instead

UsedBaggedGrad replaced by UseBaggedBoost

- like this, the use of a bagged sample in Grad-Boost or AdaBoost has the same option name

UseWeightedTrees --> removed

- it was default anyway and the only reasonable choice there is

PruneBeforeBoost --> removed

- it has been mostly a debug/trial option

NegWeightTreatment=IgnoreNegWeights --> replaced by NegWeightTreatment=IgnoreNegWeightsInTraining

- Unfortunatly the default "IgnoreNegWeights" to the BDT option "NegWeightTreatment" collided with the a global option and had to be replaced.

MethodBoost

some cleanup (removed strange experimental boosting option HighEdgeGaus, HighEdgeCoPara ..... )

remove options MethodWeightType... have it defined by the Boost Method (these have been trial options.. but for clarity it is much better to stick to the "standard" ones (i.e log(alpha) for AdaBoost etc)

up to now, the first classifier was trained with the full sample, I think however, it should also be a bagged sample (i.e. particularily if smaller sample sizes for the bagged samples were demanded) .. it's changed now, accordingly