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.
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
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:
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)
Interpreting Machine Learning Models in SAS (video how-to tutorial)
Machine Learning Fundamentals (video how-to tutorial)
Registration is open! SAS is returning to Vegas for an AI and analytics experience like no other! Whether you're an executive, manager, end user or SAS partner, SAS Innovate is designed for everyone on your team. Register for just $495 by 12/31/2023.
If you are interested in speaking, there is still time to submit a session idea. More details are posted on the website.
Data Literacy is for all, even absolute beginners. Jump on board with this free e-learning and boost your career prospects.