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Hello, loyal All Analytics readers. Whether you're a data scientist, a statistician or a decision maker with an analytical bent, we welcome you to our corner of the blogging world.
Unfortunately, the SAS sponsorship of All Analytics has come to an end. But you can find a lot of the same types of content on our SAS channels. We have blogs, discussion forums and webcasts, all covering the same analytics topics you were used to reading about on All Analytics.
What does it take to see the work we do as more than a source of income? It's about looking beyond the day-to-day tactical items of a to-do list. For our employees, it's about connecting to the significant contributions SAS makes to the world. And our own Jared Peterson experienced
The TEXT plot was introduced with SAS 9.4M2 to facilitate placement of text strings in a graph. This replaces the MARKERCHAR feature of the SCATTER plot statement, which is still available, but it is better to use TEXT plot in most cases. The syntax is: text x=column y=column text=column </
This is the fourth post in our AI interpretability series. Previous posts by my colleague Ilknur Kaynar Kabul covered partial dependence, interpreting data sets and other topics. While some machine learning models – like decision trees – are transparent, the majority of models used today – like deep neural networks, random forests, gradient boosting
In an earlier blog post my colleague, Suneel Grover, wrote about how to take advantage of the product recommendations technology implemented in SAS Customer Intelligence 360. He noted that two methods are implemented behind the scenes, a visitor-centric and a product (or item)-centric approach. And most importantly, while some sophisticated
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