up

Author: jeremiedb

README.md

@@ -143,7 +143,7 @@ shap_effects = EvoTrees.shap(m, dtrain)
 
 #### Reference
 
-Peng Yu, Chao Xu, Albert Bifet, Jesse Read Linear Tree Shap (2022). In: Proceedings of 36th Conference on Neural Information Processing Systems. https://openreview.net/forum?id=OzbkiUo24g
+Peng Yu, Chao Xu, Albert Bifet, Jesse Read Linear Tree Shap (2022). In [Proceedings of 36th Conference on Neural Information Processing Systems](https://openreview.net/forum?id=OzbkiUo24g).
 
 ## Feature importance
 

src/shap.jl

@@ -150,7 +150,7 @@ It was originally ported from this [repo](https://github.com/yupbank/linear_tree
 
 # References
 
-Peng Yu, Chao Xu, Albert Bifet, Jesse Read Linear Tree Shap (2022). In: [Proceedings of 36th Conference on Neural Information Processing Systems](https://openreview.net/forum?id=OzbkiUo24g).
+Peng Yu, Chao Xu, Albert Bifet, Jesse Read Linear Tree Shap (2022). In [Proceedings of 36th Conference on Neural Information Processing Systems](https://openreview.net/forum?id=OzbkiUo24g).
 """
 function shap(m::EvoTree, data; ntree_limit=length(m.trees))
     x_bin = EvoTrees.binarize(data; feature_names=m.info[:feature_names], edges=m.info[:edges])