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SAS Tutorial | Interpreting Machine Learning Models in SAS

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Published on Oct 14, 2019

In this SAS How To Tutorial, Ari Zitin shows you how to interpret machine learning models in SAS. After a brief introduction to model interpretability, Ari explains how to turn on model interpretability tools in SAS Viya and how to interpret a decision tree. Next, Ari uses SAS Viya Model Studio to read and interpret Partial Dependence Plots (PD), Individual Conditional Expectation Plots (ICE), and Local Interpretable Model Agnostic Explanations (LIME). Finally, Ari discusses individual observations with ICE and LIME.

You'll note that Ari uses SAS Model Studio to preform model interpretability in machine learning models in SAS.

Download data files – Use SAS to fetch the Titanic data Ari references. Get the code on GitHub – https://github.com/sascommunities/sas...

Content Outline
01:48 – Introduction to Model Interpretability
09:22 – Introduction to the data
16:10 – Partial Dependence Plots (PD)
21:43 – Individual Conditional Expectation Plots (ICE)
27:00 – Local Interpretable Model Agnostic Explanations (LIME)
35:52 – Individual Observations with ICE and LIME

Learn more about SAS Software
Interpret model predictions with partial dependence and individual conditional expectation plots – https://blogs.sas.com/content/subcons...
Improving model interpretability with LIME – https://blogs.sas.com/content/subcons...
Interpreting Black-Box Machine Learning Models Using Partial Dependence and Individual Conditional Expectation Plots – https://www.sas.com/content/dam/SAS/s...
“Why Should I Trust You? Explaining the Predictions of Any Classifier,” by Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin, Proceedings of KDD ’16 – https://arxiv.org/abs/1602.04938


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