SAS Tutorial | Machine Learning: A Coding Example in SAS
In this SAS How To Tutorial, Christa Cody shows how to create machine learning models using SAS Viya. First, you will be shown a coding example in SAS of supervised learning in SAS Studio which is great for those who are familiar with SAS programming. She will then create the same model using Model Studio machine learning pipelines. Christa’s approach may be more useful for someone not interested in programming the model themselves, but who still wants to have control over hyperparameters.
After watching this video and see the coding examples, you will have a better understanding of how to build machine learning models in SAS. If you are interested Machine Learning Fundamentals or why Christa is doing certain machine learning tasks, check out this SAS Tutorial | Machine Learning Fundamentals video ( • SAS Tutorial | Machine Learning Funda... ).
Download Data Files
Download the HMEQ data set that Christa uses http://support.sas.com/documentation/...
Chapters
00:00 – Welcome
00:40 – Example: Supervised Learning in SAS Studio
Setting variables
Load the data
Explore missing values
Partition data
Impute values
Identify variables that explain variance
Build predictive model using random forest
Score the model
Access the model using Lift and ROC information
Analyze the model
12:55 – Example: Model Studio Machine Learning Pipelines
Create new project and import data
Data tab
Pipelines tab
Run pipeline
Imputation results
Add Random Forest
Autotune Feature
View results
Assessment tab with ROC and Lift plots
Learn more about SAS Software
◉ Blog: Learn more about Christa and machine learning – https://blogs.sas.com/content/sgf/201...
◉ How-to tutorial: Machine Learning Fundamentals – • SAS Tutorial | Machine Learning Funda...
◉ Book: Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners – https://www.sas.com/store/books/categ...
◉ $79 e-Learning course: Machine Learning on SAS® Viya® – https://support.sas.com/edu/schedules...
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