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iML Package for Model Agnostic Interpretable Machine Learning

In this video the presenter goes over a new R package called iML.’ This package has a lot of power when explaining global and local feature importance. These explanations are critical, especially in the health field and if your under GDPR regulations. Now, with the combination of Shapley, LIME, and partial dependence plots, you can figure out how the model works and why.

I think we’ll see a lot of innovation in the model interpretation’ space going forward.

Notes from the video:

  • IML R package
  • ML models have huge potential but are complex and hard to understand
  • In critical conditions (life vs death), you need to explain your decision
  • Current tools for Model Interpretation: Decision Trees, Rules, Linear Regressions
  • Needs a model agnostic method
  • Feature Importance @ interpreted level for the global model
  • Compute generalization error on dataset and model
  • Scored features, what is the effect on that feature on the fitted model?
  • Fit a surrogate model
  • Generate Partial Dependence Plots (visualize the feature importance)
  • For Local Interpretation, use LIME.
  • Now part of the R as iML package (written in R 6?)
  • What’s in the iml package? Permutation Feature Importance / Feature Interactions / Partial Dependence Plots / LIME / Shapley Values / Tree Surrogates
  • Shows the bike data set example
Up next Women in FinTech: Dr. YY Huang I worked with Dr. Huang at RapidMiner for a few years and found her to be an amazing and talented data scientist. She was recently interviewed by NYC AI Event 2018 Not even two weeks in at H2o.ai and I’m already giving presentations. Boy did I misst this! Sorry for the shaky -photo. Was too excited.
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