SHAP value based Feature Dependence plot

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

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

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))
x <- head(data, 100)
shaps <- treeshap(unified_model, x)
#> 
|0%----|------|20%---|------|40%---|------|60%---|------|80%---|------|100%
#> =---------------------------------------------------------------------- (0%)

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plot_feature_dependence(shaps, variable = "overall")

# }

Arguments

Value

See also

Examples