Description Usage Arguments Value Examples
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).
1 | shap.values(xgb_model, X_train)
|
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 |
a list of three elements: the SHAP values as data.table, ranked mean|SHAP|, and BIAS
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | 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
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)
|
Petal.Length Petal.Width Sepal.Length Sepal.Width
0.62935975 0.21664035 0.02910357 0.00000000
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