Interpretable Machine Learning Using LIME Framework, blog, Driverless AI, H2O World, writers gonna write

I found this talk to be fascinating. I’ve been a big fan of LIME but never really understood the details of how it works under the hood. I understood that it works on an observation by observation basis but I never knew that it permutates data, tests against the black box model, and then builds a simple linear model to explain it.

Really cool. My notes are below the video.


  • Input > black box > output; when don’t understand the black box like neural nets
  • Example, will the loan default?
  • Typical classification problem
  • Loan and applicant information relative to historical data
  • Linear relationships are easy
  • Nonlinear relationships via a Decision Tree can still be interpreted
  • Big data creates more complexity and dimensions
  • One way to overcome this: use feature importance
  • Feature importance doesn’t give us any understanding if it’s a linear or nonlinear relationship
  • Gets better with partial dependence plots
  • Can’t do partial dependence plots for neural nets
  • You can create Bayesian Networks / shows dependencies of all variables including output variable and strength of relationship
  • Bummer: Not as accurate as some other algorithms
  • Can give you global understanding but not detailed explanation
  • Accuracy vs Interpretablity tradeoff. Does it exist?
  • Enter LIME! Local Interpretable Model-agnostic Explanations
  • At a local level, it uses a linear model to explain the prediction
  • Creates an observation, creates fakes data (permutation), then it calculates a similarity store between the fake and original data, then it takes your black box algo (neural nets?), tries different combinations of predictors
  • Takes those features with similarity scores, fits a simple model to it to define weights and scores to explain it
  • Without know what the model picks up on if it’s really signal or noise. You need LIME to verify!
  • Can apply to NLP/Text models
  • Why is it important? Trust / Predict / Improve
  • LIME helps feature engineering by none ML practitioners
  • LIME can help comply with GDPR
  • Understanding our models can help prevent vulnerable people