get_extra_comp_args_default: Gets the default values for the extra computation arguments
In shapr: Prediction Explanation with Dependence-Aware Shapley Values

get_extra_comp_args_default

R Documentation

Gets the default values for the extra computation arguments

Description

Gets the default values for the extra computation arguments

Usage

get_extra_comp_args_default(
  internal,
  paired_shap_sampling = isFALSE(internal$parameters$asymmetric),
  kernelSHAP_reweighting = "on_all_cond",
  compute_sd = isFALSE(internal$parameters$exact),
  n_boot_samps = 100,
  max_batch_size = 10,
  min_n_batches = 10
)

Arguments

`internal`	List. Not used directly, but passed through from `explain()`.
`paired_shap_sampling`	Logical. If `TRUE` paired versions of all sampled coalitions are also included in the computation. That is, if there are 5 features and e.g. coalitions (1,3,5) are sampled, then also coalition (2,4) is used for computing the Shapley values. This is done to reduce the variance of the Shapley value estimates. `TRUE` is the default and is recommended for highest accuracy. For asymmetric, `FALSE` is the default and the only legal value.
`kernelSHAP_reweighting`	String. How to reweight the sampling frequency weights in the kernelSHAP solution after sampling. The aim of this is to reduce the randomness and thereby the variance of the Shapley value estimates. The options are one of `'none'`, `'on_N'`, `'on_all'`, `'on_all_cond'` (default). `'none'` means no reweighting, i.e. the sampling frequency weights are used as is. `'on_N'` means the sampling frequencies are averaged over all coalitions with the same original sampling probabilities. `'on_all'` means the original sampling probabilities are used for all coalitions. `'on_all_cond'` means the original sampling probabilities are used for all coalitions, while adjusting for the probability that they are sampled at least once. `'on_all_cond'` is preferred as it performs the best in simulation studies, see Olsen & Jullum (2024).
`compute_sd`	Logical. Whether to estimate the standard deviations of the Shapley value estimates. This is TRUE whenever sampling based kernelSHAP is applied (either iteratively or with a fixed number of coalitions).
`n_boot_samps`	Integer. The number of bootstrapped samples (i.e. samples with replacement) from the set of all coalitions used to estimate the standard deviations of the Shapley value estimates.
`max_batch_size`	Integer. The maximum number of coalitions to estimate simultaneously within each iteration. A larger numbers requires more memory, but may have a slight computational advantage.
`min_n_batches`	Integer. The minimum number of batches to split the computation into within each iteration. Larger numbers gives more frequent progress updates. If parallelization is applied, this should be set no smaller than the number of parallel workers.