Unify randomForest model

Convert your randomForest 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.

randomForest.unify(rf_model, data)

Arguments

rf_model

An object of randomForest 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

Details

Binary classification models with a target variable that is a factor with two levels, 0 and 1, are supported

See also

lightgbm.unify for LightGBM models

gbm.unify for GBM models

xgboost.unify for XGBoost models

ranger.unify for ranger models

Examples

library(randomForest)
#> randomForest 4.7-1.1
#> Type rfNews() to see new features/changes/bug fixes.
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 <- randomForest::randomForest(target~., data = data, maxnodes = 10, ntree = 10)
unified_model <- randomForest.unify(rf, data)
shaps <- treeshap(unified_model, data[1:2,])
#> 
|0%----|------|20%---|------|40%---|------|60%---|------|80%---|------|100%
#> =---------------------------------------------------------------------- (0%)

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

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

# plot_contribution(shaps, obs = 1)