- 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

- 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

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

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

- 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

Blog post: Introductory videos for Explanatory Model Analysis with R

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 modelsâ€™ 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...*