mlr-org: Explainable machine learning with mlr3 and DALEX

Michel Lang

Przemysław Biecek and Szymon Maksymiuk added a new chapter to the mlr3 book on how to analyze machine learning models fitted with mlr3 using the excellent DALEX package.

The contributed chapter covers an analysis of a random regression forest (implemented in the ranger package) on data extracted from the FIFA video game. In more detail, the following methods for explainable machine learning are showcased:

Dataset level exploration: Feature importance and Partial dependency plots.
Instance level explanation: Break Down, SHapley Additive exPlanations (SHAP), and Ceteris Paribus plots.

Here is a small preview illustrating the effect of different features on the monetary value of Cristiano Ronaldo:

Read the complete chapter here.

Citation

For attribution, please cite this work as

Lang (2020, March 30). mlr-org: Explainable machine learning with mlr3 and DALEX. Retrieved from https://mlr-org.github.io/mlr-org-website/posts/2020-03-30-dalex/

BibTeX citation

@misc{mlr3 loves dalex,
  author = {Lang, Michel},
  title = {mlr-org: Explainable machine learning with mlr3 and DALEX},
  url = {https://mlr-org.github.io/mlr-org-website/posts/2020-03-30-dalex/},
  year = {2020}
}