The SAS Data Science Blog
Advanced analytics from SAS data scientists
In the first of a three-part series of posts, SAS' Funda Gunes and her colleague Ricky Tharrington summarize model-agnostic model interpretability in SAS Viya.
Ever since automated machine learning has entered the scene, people are asking, "Will automated machine learning replace data scientists?"
Let's talk about using DLPy to model employee retention through a survival analysis model. Survival analysis is used to model time-to-event. Examples of time-to-event include the time until an employee leaves a company, the time until a disease goes into remission, or the time until a mechanical part fails. The
Through hyperparameter autotuning, you can maximize a model's performance without maximizing effort. While SAS searches the hyperparameter space in the background, you are free to pursue other work.
Deep learning is an area of machine learning that has become ubiquitous with artificial intelligence. The complex, brain-like structure of deep learning models is used to find intricate patterns in large volumes of data. These models have heavily improved the performance of general supervised models, time series, speech recognition, object
Did you know that you can use SAS Enterprise Miner 15.1 to easily create an ASTORE? You can create an ASTORE from an HP SVM node, an HP FOREST node, and also from some SAS Viya Code Nodes! This blog will show you how.