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{shapviz} provides typical SHAP plots:

• sv_importance(): Importance plot (bar/beeswarm).
• sv_dependence() andsv_dependence2D(): Dependence plots to study feature effects and interactions.
• sv_interaction(): Interaction plot (beeswarm/bar).
• sv_waterfall(): Waterfall plot to study single or average predictions.
• sv_force(): Force plot as alternative to waterfall plot.

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, and H2O.
2. SHAP crunchers like {fastshap}, {kernelshap}, {treeshap}, {fastr}, and {DALEX}.
3. SHAP matrix and corresponding feature values.

We use {patchwork} to glue together multiple plots with (potentially) inconsistent x and/or color scale.

# From CRANinstall.packages("shapviz")# Or the newest version from GitHub:# install.packages("devtools")devtools::install_github("ModelOriented/shapviz")

Shiny diamonds... let's use XGBoost to model their prices by the four "C" variables:

library(shapviz)library(ggplot2)library(xgboost)set.seed(1)xvars<- c("log_carat","cut","color","clarity")X<-diamonds|>   transform(log_carat= log(carat))|>   subset(select=xvars)# Fit (untuned) modelfit<- xgb.train(params=list(learning_rate=0.1),data= xgb.DMatrix(data.matrix(X),label= log(diamonds$price)),nrounds=65)# SHAP analysis: X can even contain factorsX_explain<-X[sample(nrow(X),2000), ]shp<- shapviz(fit,X_pred= data.matrix(X_explain),X=X_explain)sv_importance(shp,show_numbers=TRUE)sv_importance(shp,kind="bee")sv_dependence(shp,v=xvars,share_y=TRUE)

Decompositions of individual predictions can be visualized as waterfall or force plot:

sv_waterfall(shp,row_id=2)+  ggtitle("Waterfall plot for second prediction")  sv_force(shp,row_id=2)+  ggtitle("Force plot for second prediction")

Check-out the vignettes for topics like:

• Basic use (includes working with other packages and SHAP interactions).
• Multiple models, multi-output models, and subgroup analyses.
• Plotting geographic effects.
• Working with Tidymodels.

[1] Scott M. Lundberg and Su-In Lee. A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems 30 (2017).