Merge pull request #164 from ModelOriented/h2o-random-forest

Adapt docu on h2o

Author: mayer79

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: shapviz
 Title: SHAP Visualizations
-Version: 0.9.6
+Version: 0.9.7
 Authors@R: c(
     person("Michael", "Mayer", , "mayermichael79@gmail.com", role = c("aut", "cre")),
     person("Adrian", "Stando", , "adrian.j.stando@gmail.com", role = "ctb")

NEWS.md

@@ -1,3 +1,13 @@
+# shapviz 0.9.7
+
+### Documentation
+
+- H2O random forests (regression and binary classification) are now supported as well (fast TreeSHAP) [#163](https://github.com/ModelOriented/shapviz/pull/163).
+
+### Compatibility
+
+- Adapt for upcoming {shapr} version, thanks @martinju for the fix [#162](https://github.com/ModelOriented/shapviz/pull/162).
+
 # shapviz 0.9.6
 
 ### Documentation

R/shapviz.R

@@ -5,7 +5,7 @@
 #' from a fitted model of type
 #' - XGBoost,
 #' - LightGBM, or
-#' - H2O (tree-based regression or binary classification model).
+#' - H2O (tree-based models).
 #'
 #' Furthermore, [shapviz()] can digest the results of
 #' - `fastshap::explain()`,

README.md

@@ -23,7 +23,7 @@
 
 SHAP and feature values are stored in a "shapviz" object that is built from:
 
-1. Models that know how to calculate SHAP values: XGBoost, LightGBM, H2O (boosted trees).
+1. Models that know how to calculate SHAP values: XGBoost, LightGBM, H2O (tree-based models).
 2. SHAP crunchers like {fastshap}, {kernelshap}, {treeshap}, {fastr}, and {DALEX}.
 3. SHAP matrix and corresponding feature values.
 

man/shapviz.Rd

@@ -15,7 +15,7 @@ library(usethis)
 use_description(
   fields = list(
     Title = "SHAP Visualizations",
-    Version = "0.9.6",
+    Version = "0.9.7",
     Description = "Visualizations for SHAP (SHapley Additive exPlanations),
     such as waterfall plots, force plots, various types of importance plots,
     dependence plots, and interaction plots.