SHAP value based Feature Importance plot

This function plots feature importance calculated as means of absolute values of SHAP values of variables (average impact on model output magnitude).

plot_feature_importance(
  treeshap,
  desc_sorting = TRUE,
  max_vars = ncol(shaps),
  title = "Feature Importance",
  subtitle = NULL
)

Arguments

treeshap

A treeshap object produced with the treeshap function. treeshap.object.

desc_sorting

logical. Should the bars be sorted descending? By default TRUE.

max_vars

maximum number of variables that shall be presented. By default all are presented.

title

the plot's title, by default 'Feature Importance'.

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_dependence, plot_interaction

Examples

# \donttest{
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))
shaps <- treeshap(unified_model, as.matrix(head(data, 3)))
#> 
|0%----|------|20%---|------|40%---|------|60%---|------|80%---|------|100%
#> =---------------------------------------------------------------------- (0%)

========================----------------------------------------------- (33%)

===============================================------------------------ (66%)

======================================================================= (100%)

plot_feature_importance(shaps, max_vars = 4)

# }