Interpreting Machine Learning Models

Shapley Values, MLI

I found this short 8 minute video from H2O World about Machine Learning Interpretability (MLI). It's given by Patrick Hall, the lead for building these capabilities in Driverless AI.

https://youtu.be/jlS9HGNNROQ

My notes from the video are below:

  • ML as an opaque black box is no longer the case
  • Cracking the black box with LIME and Shapley Values
  • Shapley Values won the Nobel Prize in Economics in 2012
  • After Driverless AI model runs, a dashboard is created
  • Shows the complex feature engineered and the original features
  • Global Shapley Values is like Feature Importance and includes negative and positive contributions
  • Quickly identify what are the important features in the dataset
  • Then go to Partial Dependence Plots, which are the average prediction of the model across different values of the feature
  • Row by Row analysis of each feature can be done to understand interactions and generate reason codes
  • Shapley is accurate for feature contribution, LIME is an approximation
  • Done via stacked ensemble model
  • Can be deployed via Python Scoring pipeline
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