On a mission to responsibly build
machine learning predictive models

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For R

• Install from CRAN

• install.packages("DALEX")

For Python

• Install from PyPI

• pip install dalex

EMA ebook: Explanatory Model Analysis: Explore, Explain, and Examine Predictive Models.

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API reference

R documentation with pkgdown

Python documentation with pdoc

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Examples

Explainability

• How to use DALEX with:
  keras, parsnip, caret, mlr, H2O, xgboost.
• Compare GBM models created in different languages: gbm and CatBoost in R / gbm in h2o / gbm in Python
• Comparison between BreakDown, LIME, Shapley
• Fraud detection with DALEX
• DALEX for teaching pt. 1, teaching pt. 2
• H2o AutoML with DALEX
• mlr3 benchmark with DALEX

Fairness & Interactive analysis

• Introduction to Responsible Machine Learning

Explainability

• How to use dalex with:
  xgboost, tensorflow, h2o (feat. autokeras, catboost, lightgbm)
• Introduction to the dalex package: Titanic
• Key features explained: FIFA 20
• Multioutput predictive models: Explaining multiclass classification and multioutput regression
• More explanations: residuals, shap, lime
• Introduction to the Aspect module in dalex

Fairness

• Introduction to the Fairness module in dalex
• Advanced tutorial on bias detection in dalex
• Tutorial: fairness in regression

Interactive analysis

• Introduction to the Arena module in dalex
• Arena documentation: Getting Started & Demos

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Tutorial: Basic XAI (in R & Python)

Blog: Responsible Machine Learning

• Part 1: Introduction
• Part 2: Permutation-based variable importance
• Part 3: Partial Dependence profile
• Part 4: Break Down method
• Part 5: Shapley values
• Part 6: LIME method
• Part 7: Ceteris Paribus profiles

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More materials & blogs

Materials from the Explanatory Model Analysis Workshop @ eRum 2020

Poster on fairness: Hey, ML engineer! Is your model fair? @ MLinPL 2020

Talk about Tools for Explainable AI @ X-Europe Webinars 2020

Explainable and Reproducible Machine Learning Model Development with dalex and Neptune

XAI in Python with dalex

XAI in the jungle of competing frameworks for machine learning

Python code in the form of jupyter notebooks and more

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Introductory videos

Blog post: Introductory videos for Explanatory Model Analysis with R

Introduction to the Explanatory Model Analysis

Introduction to DALEX for R

Break down in R, Break Down in Python

Shapley values in R, Ceteris paribus in R

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Reviews

Stan Lipovetsky, 2022. "Explanatory Model Analysis: Explore, Explain and Examine Predictive Models," Technometrics, 64:3, 423-424.
...The book presents a valuable collection of methods for models’ exploration and diagnostics for various machine learning algorithms. It can be useful in the data and computer science courses for students and instructors, as well as for researchers and practitioners who need to analyze and interpret their statistical and machine learning models both of glass-box and black-box kind. The book also serves as a great primary for applications of the R and Python software and their packages/libraries, so it is valuable in solving various problems of statistical prediction in various fields...

Simon French, 2022. "Explanatory model analysis: Explore, explain, and examine predictive models," Journal of the Royal Statistical Society Series A, vol. 185(3), pages 1464-1464.
...This book presents, explains, and summarises the techniques for doing so. Moreover, it provides code in R and Python for doing so. The methods have many similarities with those of sensitivity analysis developed within the Sensitivity Analysis of Model Output (SAMO) community...