Unify GBM model

Convert your GBM model into a standardized representation. The returned representation is easy to be interpreted by the user and ready to be used as an argument in treeshap() function.

gbm.unify(gbm_model, data)

Arguments

gbm_model

An object of gbm class. At the moment, models built on data with categorical features are not supported - please encode them before training.

data

Reference dataset. A data.frame or matrix with the same columns as in the training set of the model. Usually dataset used to train model.

Value

a unified model representation - a model_unified.object object

See also

lightgbm.unify for LightGBM models

xgboost.unify for XGBoost models

ranger.unify for ranger models

randomForest.unify for randomForest models

Examples

# \donttest{
library(gbm)
#> Loaded gbm 2.1.9
#> This version of gbm is no longer under development. Consider transitioning to gbm3, https://github.com/gbm-developers/gbm3
data <- fifa20$data[colnames(fifa20$data) != 'work_rate']
data['value_eur'] <- fifa20$target
gbm_model <- gbm::gbm(
             formula = value_eur ~ .,
             data = data,
             distribution = "gaussian",
             n.trees = 20,
             interaction.depth = 4,
             n.cores = 1)
unified_model <- gbm.unify(gbm_model, data)
shaps <- treeshap(unified_model, data[1:2,])
#> 
|0%----|------|20%---|------|40%---|------|60%---|------|80%---|------|100%
#> =---------------------------------------------------------------------- (0%)

====================================----------------------------------- (50%)

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

plot_contribution(shaps, obs = 1)

# }