One week after the initial release of GS 2.0 we are pleased to introduce the updated version 2.1 that sets clear difference between 2.x and 1.x branches.
Here is a comparison of processing time measured for GS 1.x and 2.x. We used datasets of 100, 1'000, 3'000 and 6'500 rows and measured single CPU-core processing time of two GS versions using the same projects and settings.
The bar chart shows that 2.x branch is 6.3 times faster by a factor of 6.3 in case of 6.5k data rows and at least not slower on a dataset with 100 rows. The Pperformance gapdifference grows fast exponentially for larger datasets. For example, 2.x learns from 200'000 rows in merely 37 minutes while 1.x can't finish the same task even within one day.
The old GS 1.x.x spends most processing time to on validationg of a model structure hypothesis about the model structure while estimation of model coefficients takes only a small portion of time. In GS 2.x we have implemented the recurrent procedure for to calculatione of testing errors that madkese the model validation stage very cheap in terms of processing time, even for large datasets. So the latest version can beis significantly faster by an order of magnitude while validation results exactly matches results of the old validation procedure.
Besides, along with improvements in processing speed in implemented in GS 2.1 we continue to improve embedded data exploration tools, and the user interface, and fix reported issues. You can read more about the latest changes in the program changelog.