Approximated SHAP values for multi-classification problem using randomForest

Question

I would like to use the fastshap package to obtain SHAP values plots for every category of my outcome in a multi-classification problem using a random forest classifier. I could only found chunks of the code around, but no explanation on how to procede from the beginning in obtaining the SHAP values in this case. Here is the code I have so far (my y has 5 classes, here I am trying to obtain SHAP values for class 3):

library(randomForest)
library(fastshap)

set.seed(42) 
sample <- sample.int(n = nrow(ITA), size = floor(.75*nrow(ITA)), replace=F)
train <- ITA [sample,]
test <- ITA [-sample,]

set.seed(42)
rftrain <-randomForest(y ~ ., data=train, ntree=500, importance = TRUE) 

p_function_3<- function(object, newdata) 
  caret::predict.train(object, 
                       newdata = newdata, 
                       type = "prob")[,3]

shap_values_G <- fastshap::explain(rftrain, 
                                   X = train, 
                                   pred_wrapper = p_function_3, 
                                   nsim = 50,
                                   newdata=train[which(y==3),])

Now, I took the code largely from an example I found online, and I tried to adapt it (I am not an expert R user), but it does not work.. Can you please help me in correcting it? Thanks!

Answer 1

Here is a working example (with a different dataset), but I think the logic is the same.

library(randomForest)
library(fastshap)

set.seed(42) 

ix <- sample(nrow(iris), 0.75 * nrow(iris))
train <- iris[ix, ]
test <- iris[-ix, ]

xvars <- c("Sepal.Width", "Sepal.Length")
yvar <- "Species"
fit <- randomForest(reformulate(xvars, yvar), data = train, ntree = 500) 

pred_3 <- function(model, newdata) {
  predict(model, newdata = newdata, type = "prob")[, "virginica"]
}

shap_values_3 <- fastshap::explain(
  fit, 
  X = train,             # Reference data
  feature_names = xvars,
  pred_wrapper = pred_3, 
  nsim = 50,
  newdata = train[train$Species == "virginica", ] # For these rows, you will calculate explanations
)

head(shap_values_3)

# Sepal.Width Sepal.Length
# <dbl>        <dbl>
# 1      0.101        0.381 
# 2      0.159       -0.0109
# 3      0.0736      -0.0285
# 4      0.0564       0.161 
# 5      0.0649       0.594 
# 6      0.232        0.0305