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How to Explain Your Black-Box Models in SAS® Viya®

Started ‎07-01-2020 by
Modified ‎07-09-2020 by
Views 2,185

Having trouble explaining machine learning models? You’re not alone. The black-box nature of some gradient boosting, forest and deep learning models are often too complex for people to understand by directly inspecting model parameters.


In this video, SAS Machine Learning Developer Funda Gunes explains various model-agnostic interpretability techniques available in SAS Viya that enable you to explain and understand machine learning models. Methods include partial dependency (PD) plots, independent conditional expectation (ICE) plots, local interpretable model-agnostic explanations (LIME), and Shapley values.



Video Highlights

0:11 – Healthcare use case
2:32 – Why machine learning models are black box
4:36 – Why model interpretability?
5:43 – Types of model interpretability methods
7:33 – Post-hoc explanations
12:24 – Partial dependence (PD) Plot
13:29 – Local interpretability
14:30 – Independent conditional expectation (ICE) plots
15:45 – Local interpretable model-agnostic explanations (LIME)
17:03 – Kernal SHAP explanation
18:14 – Accessing interpretability through programmatic usage


Read the paper


And here's a related a video tutorial, Opening the Black Box of Model Interpretability in SAS® Viya® that goes into many of the same topics, including gradient boosting:



Video Highlights

02:01  – Overview of Model Interpretability

03:54  – Gradient Boosting model

05:30  – Building a Modeling Pipeline and Exploring Interpretable Models in SAS Viya

14:23  – Calculating Partial Dependence Plots

16:57  – Exploring Individual Conditional Expectation Plots

23:51  – Local Interpretable Model-Agnostic Explanations (LIME Plot) overview

27:19  – Kernel Shapley Additive Explanations (SHAP Plot)


Related Resources

How to explain your black-box models in SAS® Viya®

Interpreting Machine Learning Models in SAS (video how-to tutorial)

Machine Learning Fundamentals (video how-to tutorial)

SAS® Visual Data Mining and Machine Learning Documentation

Explain Model Action Set

Github repository

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Last update:
‎07-09-2020 04:28 PM
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