shap.prep.interaction | R Documentation |
shap.prep.interaction
just runs shap_int <- predict(xgb_mod, (X_train), predinteraction = TRUE)
, thus it may not be necessary.
Read more about the xgboost predict function at xgboost::predict.xgb.Booster
. Note that this functionality is unavailable for LightGBM models.
shap.prep.interaction(xgb_model, X_train)
xgb_model |
a xgboost model object |
X_train |
the dataset of predictors used for the xgboost model |
a 3-dimention array: #obs x #features x #features
# To get the interaction SHAP dataset for plotting:
# fit the xgboost model
# options("Ncup" = 1)
mod1 = xgboost::xgboost(
data = as.matrix(iris[,-5]), label = iris$Species,
gamma = 0, eta = 1, lambda = 0, nrounds = 1, verbose = FALSE, nthread = 1)
# Use either:
data_int <- shap.prep.interaction(xgb_mod = mod1,
X_train = as.matrix(iris[,-5]))
# or:
shap_int <- predict(mod1, as.matrix(iris[,-5]),
predinteraction = TRUE)
# **SHAP interaction effect plot **
shap.plot.dependence(data_long = shap_long_iris,
data_int = shap_int_iris,
x="Petal.Length",
y = "Petal.Width",
color_feature = "Petal.Width")
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