RegressionVariance Calculate the "SeparationGain" for Regression analysis separation critiera used in various training algorithms There are two things: the Separation Index, and the Separation Gain Separation Index: Measure of the "Variance" of a sample. Separation Gain: the measure of how the quality of separation of the sample increases by splitting the sample e.g. into a "left-node" and a "right-node" (N * Index_parent) - (N_left * Index_left) - (N_right * Index_right) this is then the quality crition which is optimized for when trying to increase the information in the system (making the best selection
| virtual | ~RegressionVariance() |
| static TClass* | Class() |
| TString | GetName() |
| Double_t | GetSeparationGain(const Double_t& nLeft, const Double_t& targetLeft, const Double_t& target2Left, const Double_t& nTot, const Double_t& targetTot, const Double_t& target2Tot) |
| virtual Double_t | GetSeparationIndex(const Double_t& n, const Double_t& target, const Double_t& target2) |
| virtual TClass* | IsA() const |
| TMVA::RegressionVariance& | operator=(const TMVA::RegressionVariance&) |
| TMVA::RegressionVariance | RegressionVariance() |
| TMVA::RegressionVariance | RegressionVariance(const TMVA::RegressionVariance& s) |
| virtual void | ShowMembers(TMemberInspector& insp) const |
| virtual void | Streamer(TBuffer&) |
| void | StreamerNVirtual(TBuffer& ClassDef_StreamerNVirtual_b) |
| TString | fName | name of the concrete Separation Index impementation |
| Inheritance Chart: | |||||
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Return the gain in separation of the original sample is splitted in two sub-samples (N * Index_parent) - (N_left * Index_left) - (N_right * Index_right)
Return the separation index (a measure for "purity" of the sample") Return the name of the concrete Index implementation
{ return fName; }