Gets the default values for the extra estimation arguments

Source: R/setup.R

Gets the default values for the extra estimation arguments

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.

Author

Martin Jullum