Custom XGBoost Loss function w/ Zygote. Julia Computing blog post

yeah. all the problems i’m working right now is for classification tasks. but it’s easy to add the regression metric for crossvalidation. you can look at the skcrossvalidator.jl source (AutoMLPipeline.jl/skcrossvalidator.jl at master · IBM/AutoMLPipeline.jl · GitHub). I’m using these metrics: 3.3. Metrics and scoring: quantifying the quality of predictions — scikit-learn 1.1.2 documentation.

i’ll create an issue to add regression support. I focus in classification as regression is more trivial to implement once classification workflow works. also, feel free to make a PR ;).

you can differentiate classifiers from regressors because their names contain a substring of either Classifier or Regressor .