set_reference_dataset: Set reference dataset

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

Set reference dataset

Description

Change a dataset used as reference for calculating SHAP values. Reference dataset is initially set with data argument in unifying function. Usually reference dataset is dataset used to train the model. Important property of reference dataset is that SHAP values for each observation add up to its deviation from mean prediction for a reference dataset.

Usage

set_reference_dataset(unified_model, x)

Arguments

unified_model	Unified model representation of the model created with a (model).unify function. (model_unified.object).
x	Reference dataset. A data.frame or matrix with the same columns as in the training set of the model.

Value

model_unified.object. Unified representation of the model as created with a (model).unify function, but with changed reference dataset (Cover column containing updated values).

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(gbm)
data <- fifa20$data[colnames(fifa20$data) != 'work_rate']
data['value_eur'] <- fifa20$target
gbm_model <- gbm::gbm(
formula = value_eur ~ .,
  data = data,
  distribution = "laplace",
  n.trees = 20,
  interaction.depth = 4,
  n.cores = 1)
unified <- gbm.unify(gbm_model, data)
set_reference_dataset(unified, data[200:700, ])

treeshap documentation built on May 29, 2024, 1:27 a.m.