I wrote code for doing rank-scaling. This scaling technique is more robust than StandardScaler (unit variance, zero mean).
https://github.com/scikit-learn/scikit-learn/pull/2176 I believe that "scale" is the wrong term for this operation. It's actually feature "normalization". This name-conflicts with the "normalize" method, though. I wrote documentation and tests. However, I was unable to get the doc-suite or test-suite to build for the current sklearn HEAD, so I couldn't double-check all my documentation and tests. -- Joseph Turian, Ph.D. | President, MetaOptimize "Optimize Profits. Optimize Engagement." http://metaoptimize.com 855-ALL-DATA The web's best forum for data scientists: http://metaoptimize.com/qa/ ------------------------------------------------------------------------------ See everything from the browser to the database with AppDynamics Get end-to-end visibility with application monitoring from AppDynamics Isolate bottlenecks and diagnose root cause in seconds. Start your free trial of AppDynamics Pro today! http://pubads.g.doubleclick.net/gampad/clk?id=48808831&iu=/4140/ostg.clktrk _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
