Unify ranger model

Convert your ranger 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.

ranger.unify(rf_model, data)

Arguments

rf_model

An object of ranger 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

gbm.unify for GBM models

xgboost.unify for XGBoost models

randomForest.unify for randomForest models

Examples

 library(ranger)
#> 
#> Attaching package: ‘ranger’
#> The following object is masked from ‘package:randomForest’:
#> 
#>     importance
 data_fifa <- fifa20$data[!colnames(fifa20$data) %in%
                            c('work_rate', 'value_eur', 'gk_diving', 'gk_handling',
                             'gk_kicking', 'gk_reflexes', 'gk_speed', 'gk_positioning')]
 data <- na.omit(cbind(data_fifa, target = fifa20$target))

 rf <- ranger::ranger(target~., data = data, max.depth = 10, num.trees = 10)
 unified_model <- ranger.unify(rf, data)
 shaps <- treeshap(unified_model, data[1:2,])
#> 
|0%----|------|20%---|------|40%---|------|60%---|------|80%---|------|100%
#> =---------------------------------------------------------------------- (0%)

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

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

 plot_contribution(shaps, obs = 1)