How can I get lime plot for a specific observation in sas viya 3.5?

[Ullsokk]

How do I get a LIME explanation for a specific observation is sas viya 3.5? I want to be able to explain why a specific customer has a high probability value on a model.  

[sbxkoenk]

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@Ullsokk wrote:

How do I get a LIME explanation for a specific observation is sas viya 3.5? I want to be able to explain why a specific customer has a high probability value on a model.  

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I moved this topic to "Data Science board".

A LIME explanation for one specific observation ??
To explain your high probability value you need to estimate Shapley values!

Run the shapleyExplainer action in PROC CAS (HyperSHAP and KernelSHAP methods are available).

In this blog they explain how to do : Parallel Shapley value estimation
https://blogs.sas.com/content/subconsciousmusings/2021/07/06/tips-for-parallel-processing-in-casl/

(it might be worthwhile to do it in parallel because it takes a lot of resources

, so if you do not have a lot of resources --> then it takes time)

Good luck,
koen

[sbxkoenk]

On top of previous response ...

Subject: interpreting complex (machine learning) models

• partial dependency plots
• variable importance plots / Variable Importance Rankings
• LIME (Local Interpretable Model Agnostic Explanations)
• ICE (Individual Conditional Expectation)
• SHAPLEY values

Some resources (#5):

• LIME and ICE in SAS Viya 3.4: Interpreting Machine Learning Models (article)
  https://communities.sas.com/t5/SAS-Communities-Library/LIME-and-ICE-in-SAS-Viya-3-4-Interpreting-Mac...
  See also REFERENCES and ADDITIONAL RESOURCES in the bottom of article!
• Paper SAS1950-2018
  Interpreting Black-Box Machine Learning Models Using Partial Dependence and Individual Conditional Expectation Plots
  Ray Wright, SAS Institute Inc.
  https://www.sas.com/content/dam/SAS/support/en/sas-global-forum-proceedings/2018/1950-2018.pdf
• SAS Tutorial | Interpreting Machine Learning Models in SAS (video van 41m10s)
  https://www.youtube.com/watch?v=6LcyVSLwVck
• Communicating Analytical Results and Interpreting Machine Learning Models with SAS Viya (video van 44m11s)
  https://www.youtube.com/watch?v=Ql5oB-CDFGU
• Model Interpretability Plots - SAS Help Center
  https://go.documentation.sas.com/?docsetId=vdmmladvug&docsetTarget=n0dqb7uyhlqogvn16xnengpro0fc.htm&...

Enjoy!

Koen

[xinhunt]

As Koen has explained in an earlier reply, Shapley values may be more appropriate for your use case because Shapley values give you the importance of each input variable for a prediction. LIME values tells you "how much would I expect the prediction to change if I change one/some of the inputs near this observation" which is not exactly the same as feature importance, but could be useful for different scenarios.

To calculate Shapley values or LIME values, you can use the explainModel action set. Here are some links for code examples -- all links are for Viya3.5:

(1) Example for calculating Shapley values using ShapleyExplainer (HyperSHAP, preferred method for Shapley value estimation) 

(2) Example for calculating LIME values

(3) Example for calculating Shapley values using linearExplainer (KernelSHAP, can be less accurate than HyperSHAP, but in case you need an alternative method):