Skip to content
Code examples and supporting materials for data mining and machine learning techniques on the SAS Viya environment.
SAS Python R
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
data add telecom data Mar 27, 2018
open_source_code_node Sample code to plot using ggplot2 Feb 27, 2019
sas_code_node make model assessement code more generic Jun 27, 2019
LICENSE Initial commit Feb 28, 2018
README.md minor update to readme Sep 21, 2018
SUPPORT.md Add support file Mar 22, 2019

README.md

Summary

SAS Visual Data Mining and Machine Learning (SAS VDMML) provides a comprehensive, collaborative visual interface for accomplishing all steps related to the analytical life cycle on a massively parallel processing infrastructure. The machine learning pipelines in Model Studio (that is part of SAS VDMML) lets you explore and compare multiple modeling approaches rapidly. You can quickly and easily find the optimal parameter settings for diverse machine learning algorithms – including decision trees, random forests, gradient boosting, neural networks, support vector machines and factorization machines – simply by selecting the option you want. Complex local search optimization routines work hard in the background to efficiently and effectively tune your models.

Additional resources

Contributors: Wendy Czika, Christian Medins, Radhikha Myneni, Ray Wright and Brett Wujek

You can’t perform that action at this time.