Explainable machine learning with mlr3 and DALEX

[This article was first published on r-bloggers on Machine Learning in R, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

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.

To leave a comment for the author, please follow the link and comment on their blog: r-bloggers on Machine Learning in R.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.



If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.

Search R-bloggers

Sponsors

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)