randomForest.unify: Unify randomForest model

In ModelOriented/treeshap: Compute SHAP Values for Your Tree-Based Models Using the 'TreeSHAP' Algorithm

Unify randomForest model

Description

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.

Usage

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.

Details

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

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

ranger.unify for ranger models

Examples

library(randomForest)
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,])
# plot_contribution(shaps, obs = 1)

ModelOriented/treeshap documentation built on Jan. 27, 2024, 10:57 p.m.