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

Author

Martin Jullum