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DC2 C++ decision trees

DC2 C++ decision trees. Quick review of classification (or decision) trees Training and testing How Bill does it with Insightful Miner Applications to the “good-energy” trees: how does it compare?. Toby Burnett Frank Golf. predicate. predicate. purity. Quick Review of Decision Trees.

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DC2 C++ decision trees

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  1. DC2 C++ decision trees • Quick review of classification (or decision) trees • Training and testing • How Bill does it with Insightful Miner • Applications to the “good-energy” trees: how does it compare? Toby Burnett Frank Golf DC2 at GSFC - 28 Jun 05 - T. Burnett

  2. predicate predicate purity Quick Review of Decision Trees • Introduced to GLAST by Bill Atwood, using InsightfulMiner • Each branch node is a predicate, or cut on a variable, likeCalCsIRLn > 4.222 • If true, this defines the right branch, otherwise the left branch. • If there is no branch, the node is a leaf; a leaf contains the purity of the sample that reaches that point • Thus the tree defines a function of the event variables used, returning a value for the purity from the training sample true: right false: left DC2 at GSFC - 28 Jun 05 - T. Burnett

  3. Training and testing procedure • Analyze a training sample containing a mixture of “good” and “bad” events: I use the even events in order to have an independent set for testing • Choose set of variables and find the optimal cut for such that the left and right subsets are purer than the orignal. Two standard criteria for this: “Gini” and entropy. I currently use the former. • WS : sum of signal weights • WB : sum of background weights Gini = 2 WS WB/(WS +WB) Thus Gini wants to be small. Actually maximize the improvement:Gini(parent)-Gini(left child)-Gini(right child) • Apply this recursively until too few events. (100 for now) • Finally test with the odd events: measure purity for each node DC2 at GSFC - 28 Jun 05 - T. Burnett

  4. Evaluate by Comparing with IM results From Bill’s Rome ’03 talk: The “good cal” analysis CAL-Low CT Probabilities CAL-High CT Probabilities All Good Good All Good Bad Bad DC2 at GSFC - 28 Jun 05 - T. Burnett

  5. Compare with Bill, cont • Since Rome: • Three energy ranges, three trees. • CalEnergySum: 5-350; 350-3500; >3500 • Resulting decision trees implemented in Gleam by a “classification” package: results saved to the IMgoodCal tuple variable. • Development until now for training, comparison: the all_gamma from v4r2 • 760 K events E from 16 MeV to 160 GeV (uniform in log(E), and 0<  < 90 (uniform in cos() ). • Contains IMgoodCal for comparison • Now: DC2 at GSFC - 28 Jun 05 - T. Burnett

  6. Bill-type plots for all_gamma DC2 at GSFC - 28 Jun 05 - T. Burnett

  7. Another way of looking at performance Define efficiency as fraction of good events after given cut on node purity; determine bad fraction for that cut DC2 at GSFC - 28 Jun 05 - T. Burnett

  8. The classifier package • Properties • Implements Gini separation (entropy now implemented) • Reads ROOT files • Flexible specification of variables to use • Simple and compact ascii representation of decision trees • Recently implemented: multiple trees, boosing • Not yet: pruning • Advantages vs IM: • Advanced techniques involving multiple trees, e.g. boosting, are available • Anyone can train trees, play with variables, etc. DC2 at GSFC - 28 Jun 05 - T. Burnett

  9. Growing trees • For simplicity, run a single tree: initially try all of Bill’s classification tree variables: list at right. • The run over the full 750 K events, trying each of the 70 variables takes only a few minutes! DC2 at GSFC - 28 Jun 05 - T. Burnett

  10. Performance of the truncated list 1691 leaf nodes 3381 total Note: this is cheating: evaluation using the same events as for training DC2 at GSFC - 28 Jun 05 - T. Burnett

  11. Separate tree comparison IM classifier DC2 at GSFC - 28 Jun 05 - T. Burnett

  12. Current status: boosting works! Example using D0 data DC2 at GSFC - 28 Jun 05 - T. Burnett

  13. Current status, next steps • The current classifier “goodcal” single-tree algorithm applied to all energies is slightly better than the three individual IM trees • Boosting will certainly improve the result • Done: • One-track vs. vertex: which estimate is better? • In progress in Seattle (as we speak) • PSF tail suppression: 4 trees to start. • In progress in Padova (see F. Longo’s summary) • Good-gamma prediction, or background rejection • Switch to new all_gamma run, rename variables. DC2 at GSFC - 28 Jun 05 - T. Burnett

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