get_iterative_args_default: Function to specify arguments of the iterative estimation...
In shapr: Prediction Explanation with Dependence-Aware Shapley Values

get_iterative_args_default

R Documentation

Function to specify arguments of the iterative estimation procedure

Description

Function to specify arguments of the iterative estimation procedure

Usage

get_iterative_args_default(
  internal,
  initial_n_coalitions = ceiling(min(200, max(5, internal$parameters$n_features,
    (2^internal$parameters$n_features)/10), internal$parameters$max_n_coalitions)),
  fixed_n_coalitions_per_iter = NULL,
  max_iter = 20,
  convergence_tol = 0.02,
  n_coal_next_iter_factor_vec = c(seq(0.1, 1, by = 0.1), rep(1, max_iter - 10))
)

Arguments

`internal`	List. Not used directly, but passed through from `explain()`.
`initial_n_coalitions`	Integer. Number of coalitions to use in the first estimation iteration.
`fixed_n_coalitions_per_iter`	Integer. Number of `n_coalitions` to use in each iteration. `NULL` (default) means setting it based on estimates based on a set convergence threshold.
`max_iter`	Integer. Maximum number of estimation iterations
`convergence_tol`	Numeric. The t variable in the convergence threshold formula on page 6 in the paper Covert and Lee (2021), 'Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regression' https://arxiv.org/pdf/2012.01536. Smaller values requires more coalitions before convergence is reached.
`n_coal_next_iter_factor_vec`	Numeric vector. The number of `n_coalitions` that must be used to reach convergence in the next iteration is estimated. The number of `n_coalitions` actually used in the next iteration is set to this estimate multiplied by `n_coal_next_iter_factor_vec[i]` for iteration `i`. It is wise to start with smaller numbers to avoid using too many `n_coalitions` due to uncertain estimates in the first iterations.

Details

The functions sets default values for the iterative estimation procedure, according to the function defaults. If the argument iterative of explain() is FALSE, it sets parameters corresponding to the use of a non-iterative estimation procedure