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ACM Talk on February 28 Monday at LinkedIn (Mountain View, CA)
Title: Heuristic Design of Experiments with Meta-Gradient Search of Model Training Parameters http://www.sfbayacm.org/?p=2464 LOCATION: LinkedIn, 2025 Stierlin Ct, Mountain View, CA 94043 Date: Monday February 28, 2011; 6:30 pm 6:30 – 9:00 pm (6:30 – 7:00 networking & snacks; 7:00 – 7:10 announcements; 7:10+ presentation, Q&A) Cost: Free and open to all who wish to attend, but membership is only $20/year. Anyone may join our mailing list at no charge, and receive announcements of upcoming events. Speakers: Greg Makowski Title: Heuristic Design of Experiments with Meta-Gradient Search of Model Training Parameters
Key questions discussed include: as a data miner with many algorithms and software available, how to stay organized with all the choices that can be varied during a project? Choices to search frequently include a) algorithm parameters, b) cost-profit (related to Type 1 vs 2) error bias, c) definition of the target field, d) boosting, bagging, ensemble model combining or stacking, and e) iterating over data versions in an Agile process. How should you plan, how can you best learn as you go? Should you constrain your algorithm choices if you need to describe your resulting data mining system?
As an example, SAS Enterprise Miner’s model training parameters are organized in a “scientific or laboratory notebook” for computational experiments, what I call a “model notebook” data structure to help plan a Design Of Experiments (DOE). A meta-heuristic search process is described to plan and search the many model parameters and data mining choices. The search process is related to gradient descent, only on model training parameters and project choices instead of on model weights. A brief overview of sensitivity analysis is provided to describe how any arbitrarily complex system can be described to a reasonable level of detail, both globally and at the record level (if you need reason codes for each forecast produced).
Greg Makowski is the Director of Risk Analytics and Policy at CashEdge, in Sunnyvale, CA. His data mining group forecasts fraud detection and identity theft for electronic funds transfer. CashEdge integrates as a SaaS with over 700 banks providing features like Pay Other People (with your cell phone or email), mov
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