unify {treeshap}

Unify tree-based model

Description

Convert your tree-based 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.

Usage

unify(model, data, ...)

Arguments

model

A tree-based model object of any supported class (gbm, lgb.Booster, randomForest, ranger, or xgb.Booster).

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.

...

Additional parameters passed to the model-specific unification functions.

Value

A unified model representation - a model_unified.object object (for single-output models) or model_unified_multioutput.object, which is a list of model_unified.object objects (for multi-output models).

See Also

lightgbm.unify for LightGBM models

gbm.unify for GBM models

xgboost.unify for XGBoost models

ranger.unify for ranger models

randomForest.unify for randomForest models

Examples

 library(ranger)
 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))

 rf1 <- ranger::ranger(target~., data = data, max.depth = 10, num.trees = 10)
 unified_model1 <- unify(rf1, data)
 shaps1 <- treeshap(unified_model1, data[1:2,])
 plot_contribution(shaps1, obs = 1)

 rf2 <- randomForest::randomForest(target~., data = data, maxnodes = 10, ntree = 10)
 unified_model2 <- unify(rf2, data)
 shaps2 <- treeshap(unified_model2, data[1:2,])
 plot_contribution(shaps2, obs = 1)