Lecture 1 Additional Software Tools

• Python:
  • causalml
  • interpret
  • imodels
  • PiML-Toolbox
  • sklearn-expertsys
  • skope-rules
  • tensorflow/lattice
• R:
  • arules
  • elasticnet
  • gam
  • glmnet
  • quantreg
  • rpart
  • RuleFit
• Python, R or other:
  • h2o-3
  • Rudin Group code
  • xgboost

Lecture 1 Additional Software Examples

• Building from Penalized GLM to Monotonic GBM (simple)
• Building from Penalized GLM to Monotonic GBM
• Simple Explainable Boosting Machine Example
• PiML Assignment 1 Example and simple requirements.txt
• Machine Learning for High-risk Applications: Use Cases (Chapter 6)

Lecture 1 Additional Reading

• Introduction and Background:
  • An Introduction to Machine Learning Interpretability
  • Designing Inherently Interpretable Machine Learning Models
  • Psychological Foundations of Explainability and Interpretability in Artificial Intelligence
  • Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead
• Explainable Machine Learning Techniques:
  • Accurate Intelligible Models with Pairwise Interactions
  • Elements of Statistical Learning - Chapters 3,4, and 9
  • Fast Interpretable Greedy-Tree Sums (FIGS)
  • Interpretable Machine Learning - Chapter 5
  • GAMI-Net: An Explainable Neural Network Based on Generalized Additive Models with Structured Interactions
  • Neural Additive Models: Interpretable Machine Learning with Neural Nets
  • A Responsible Machine Learning Workflow with Focus on Interpretable Models, Post-hoc Explanation, and Discrimination Testing
  • This Looks Like That: Deep Learning for Interpretable Image Recognition
  • Unwrapping The Black Box of Deep ReLU Networks: Interpretability, Diagnostics, and Simplification