Unify tree-based model

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.

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

gpboost.unify for GPBoost models

gbm.unify for GBM models

xgboost.unify for XGBoost models

ranger.unify for ranger models

randomForest.unify for randomForest models

Examples

if (
  requireNamespace("ranger", quietly = TRUE) &&
    requireNamespace("randomForest", quietly = TRUE)
) {
  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)
}
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