plot_feature_dependence: SHAP value based Feature Dependence plot

In treeshap: Compute SHAP Values for Your Tree-Based Models Using the 'TreeSHAP' Algorithm

SHAP value based Feature Dependence plot

Description

Depending on the value of a variable: how does it contribute into the prediction?

Usage

plot_feature_dependence(
  treeshap,
  variable,
  title = "Feature Dependence",
  subtitle = NULL
)

Arguments

treeshap	A treeshap object produced with the treeshap function. treeshap.object.
variable	name or index of variable for which feature dependence will be plotted.
title	the plot's title, by default 'Feature Dependence'.
subtitle	the plot's subtitle. By default no subtitle.

Value

a ggplot2 object

See Also

treeshap for calculation of SHAP values

plot_contribution, plot_feature_importance, plot_interaction

Examples

library(xgboost)
data <- fifa20$data[colnames(fifa20$data) != 'work_rate']
target <- fifa20$target
param <- list(objective = "reg:squarederror", max_depth = 3)
xgb_model <- xgboost::xgboost(as.matrix(data), params = param, label = target,
                              nrounds = 20, verbose = FALSE)
unified_model <- xgboost.unify(xgb_model, as.matrix(data))
x <- head(data, 100)
shaps <- treeshap(unified_model, x)
plot_feature_dependence(shaps, variable = "overall")

treeshap documentation built on May 29, 2024, 1:27 a.m.