Neural Market Trends |||

Interpretable Machine Learning Using LIME Framework

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
Up next Millennials are Bailing on their jobs I find this somewhat older post fascinating. It would appear that more and more millennials are waking up and realizing that there’s more to life Throwback to Gartner 2017
Latest posts Phone Addiction Version 12 Launches Today! Machine Learning Making Pesto Tastier 5 Dangerous Things You Should Let Your Kids Do The Pyschology of Writing TensorFlow and High Level APIs Driving Marketing Performance with H2O Driverless AI Machine Learning and Data Munging in H2O Driverless AI with datatable Making AI Happen Without Getting Fired Latest Musings from a Traveling Sales Engineer The Night before H2O World 2019 Why Forex Trading is Frustrating Functional Programming in Python Automatic Feature Engineering with Driverless AI Ray Dalio's Pure Alpha Fund What's new in Driverless AI? Latest Writings Elsewhere - December 2018 House Buying Guide for Millennials Changing Pinboard Tags with Python Automate Feed Extraction and Posting it to Twitter Flux: A Machine Learning Framework for Julia Getting Started in Data Science Part 2 Makers vs Takers How Passive Investing Saved My Life Startups and Open Source The Process of Writing H2O AI World 2018 in London Ray Dalio's Pure Alpha Fund Isolation Forests in Living the Dream? Humility and Equanimity in Sales