Interpreting Machine Learning Models

Shapley Values, MLIShapley 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.

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
Up next Microsoft the AI Powerhouse Algorithmic trading in less than 100 lines of Python code - O’Reilly Media
Latest posts Revisiting GOOG, GE, NE, IYR from 2007 The Ye Old Blog List Motorola: Then and Now EWM Redux Testing for mean reversion with Python & developing simple VIX system - Talaikis unsorted - Tadas Talaikis Blog Steps to calculate centroids in cluster using K-means clustering algorithm - Data Science Central Basics of Statistical Mean Reversion Testing - QuantStart Algorithmic trading in less than 100 lines of Python code - O’Reilly Media Interpreting Machine Learning Models Microsoft the AI Powerhouse Investing in the S&P500 still beats AI Trading Microsoft makes a push to simplify machine learning | TechCrunch 10 Great Articles On Python Development — Hacker Noon Introduction to Keras Democratising Machine learning with H2O — Towards Data Science Getting started with Python datatable | Kaggle Phone Addiction Machine Learning Making Pesto Tastier 5 Dangerous Things You Should Let Your Kids Do The Pyschology of Writing Investing in 2019 and beyond 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