How to use Suggestions in SAS® Data Studio
SAS Technical Training Consultant Mary Kathryn Queen introduces you to SAS Data Studio's Suggestions feature.
Monthly Archives: February, 2020
Introducing the feature contribution index for model assessment
Most model assessment metrics, such as Lift, AUC, KS, ASE, require the presence of the target/label to be in the data. This is always the case at the time of model training. But how can I ensure that the developed model can be applied to new data for prediction?
Creating a model validation pipeline
This post describes a fully automated validation pipeline for analytical models as part of an analytical platform, which has been set up recently as part of a customer project.
Model validation testing in the age of DevOps
Let us now take a look at a well-known metaphor for test case development in the software industry. We are referring to the idea of the “test pyramid."
A structured approach to model validation
In total, there are four posts in this blog series, this is the first post describing some basic principles of the DevOps (or ModelOps) approach.
An Easy Button for Data Science in Python
This blog is a part of a series on the Data Science Pilot Action Set. In this blog we review all nine actions in Python. Have you noticed the button bar in the upper right-hand corner of the SAS Visual Data Mining and Machine Learning Programming Guide? This button bar
Kick-start model pipelines using SAS® Visual Analytics
Starting your data scientist journey? Want to build your own predictive models? SAS' Xavier Bizoux shows you how to use SAS Visual Analytics to identify which model likely to perform the best.
Easier Feature Generation and New Data Science Pilot Actions
This blog is a part of a series on the Data Science Pilot Action Set. In this blog, we discuss updates to Visual Data Mining and Machine Learning with the release of Viya 3.5. In the middle of my blog series, SAS released Viya 3.5. Included in Viya 3.5 was the