This post introduces new Python and R functionality how to get a quick summary of any model.

Causal forests model treatment effect inhomogeneity. We use XAI tools to interpret such model to see which features are associated with the treatment effect.

Multivariate imputations with missRanger.

This post investigates properties of SHAP values of additive models.

In this post, we improve a simple GLM by insights from a boosted trees model.

In this post you will learn how to explain a {tidymodels} blackbox with classic XAI and SHAP.

This question sends shivers down the poor modelers spine… The {hstats} R package introduced in our last post measures their strength using Friedman’s H-statistics, a collection of statistics based on partial dependence functions. On Github, the preview version of {hstats} 1.0.0 out – I will try to bring it to CRAN ...

What makes a ML model a black-box? It is the interactions. Without any interactions, the ML model is additive and can be exactly described. Studying interaction effects of ML models is challenging. The main XAI approaches are: This post is mainly about the third approach. Its beauty is that we ...

"R Python" continued... Geographic SHAP

tidymodels and shapviz to explain XGBoost models

How to get "dplyr" feeling without "dplyr"

Peaking into richly parametrized linear models with SHAP? Yes!

"R Python" continued... Kernel SHAP

Standard Kernel SHAP has arrived in R. We show how well it plays together with deep learning in Keras

The "shapviz" package now plays well together with H2O.

Visualize SHAP values without tears.

How to combine model interpretation package "flashlight" with "plotly"?

"R Python" continued... DuckDB: Quacking SQL

It must have been around the year 2000, when I wrote my first snipped of SPLUS/R code. One thing I've learned back then: Loops are slow. Replace them with vectorized calculations or if vectorization is not possible, use sapply() et al. Since then, the R core team and the community ...