Prescriptive Analytics
This extension offers an operator to do prescriptive optimization. This means you vary the values of an example to optimize a custom fitness function which may derive from a model. Currently supported optimizers: - Grid - Evolutionary - BYOBA <br/> Note: This is a BETA version!
Version 0.16
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- Added proper error messages if the start value is out of bounds
- The error messages of the inner processes are now properly shown in the GUI.
Version 0.1.5
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- Added an option to add get candidate results. I.e. more than just the best solution
- Evolutionary is now correctly using custom random seeds - Added Powell as an unbounded optimization method
- A lot of code improvement for optimizer creation and parameter handling
Version 0.1.4
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- Added "random", "input example" and "reference example" as a starting point options.
- Removed the start setting in SimpleBounds. Please provide start values via input/reference exa. E.g. via Set Data.
- Added a few better error messages for missing references, missing bounds etc
Version 0.1.3
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- Added a number of interpolation point default boolean for BYOBA
- Added CMA-ES as a optimization method
Product Details
Version | 0.1.6 |
File size | 773 kB |
Downloads | 8167 (5 Today) |
Vendor | RapidMiner Labs |
Category | Machine Learning |
Released | 9/10/19 |
Last Update | 9/10/19 8:00 AM |
License | AGPL |
Product web site | rapidminer.com |
Rating | (0)
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