Get SHAP scores from a trained XGBoost or LightGBM model

shap.values returns a list of three objects from XGBoost or LightGBM model: 1. a dataset (data.table) of SHAP scores. It has the same dimension as the X_train); 2. the ranked variable vector by each variable's mean absolute SHAP value, it ranks the predictors by their importance in the model; and 3. The BIAS, which is like an intercept. The rowsum of SHAP values including the BIAS would equal to the predicted value (y_hat).

shap.values(xgb_model, X_train)

Arguments

xgb_model	an XGBoost or LightGBM model object
X_train	the dataset of predictors (independent variables) used for calculating SHAP values, it should be a matrix

Value

a list of three elements: the SHAP values as data.table, ranked mean|SHAP|, and BIAS

Examples

data("iris")
X1 = as.matrix(iris[,-5])
mod1 = xgboost::xgboost(
  data = X1, label = iris$Species, gamma = 0, eta = 1,
  lambda = 0, nrounds = 1, verbose = FALSE)

# shap.values(model, X_dataset) returns the SHAP
# data matrix and ranked features by mean|SHAP|
shap_values <- shap.values(xgb_model = mod1, X_train = X1)
shap_values$mean_shap_score
#> Petal.Length  Petal.Width Sepal.Length  Sepal.Width 
#>   0.62935975   0.21664035   0.02910357   0.00000000 
shap_values_iris <- shap_values$shap_score

# shap.prep() returns the long-format SHAP data from either model or
shap_long_iris <- shap.prep(xgb_model = mod1, X_train = X1)
# is the same as: using given shap_contrib
shap_long_iris <- shap.prep(shap_contrib = shap_values_iris, X_train = X1)

# **SHAP summary plot**
shap.plot.summary(shap_long_iris, scientific = TRUE)
shap.plot.summary(shap_long_iris, x_bound  = 1.5, dilute = 10)

# Alternatives options to make the same plot:
# option 1: from the xgboost model
shap.plot.summary.wrap1(mod1, X = as.matrix(iris[,-5]), top_n = 3)

# option 2: supply a self-made SHAP values dataset
# (e.g. sometimes as output from cross-validation)
shap.plot.summary.wrap2(shap_score = shap_values_iris, X = X1, top_n = 3)