check_categorical_valid_MCsamp: Check that all explicands has at least one valid MC sample in...

In shapr: Prediction Explanation with Dependence-Aware Shapley Values

Check that all explicands has at least one valid MC sample in causal Shapley values

Description

Check that all explicands has at least one valid MC sample in causal Shapley values

Usage

check_categorical_valid_MCsamp(dt, n_explain, n_MC_samples, joint_prob_dt)

Arguments

dt

Data.table containing the generated MC samples (and conditional values) after each sampling step

n_MC_samples

Positive integer. For most approaches, it indicates the maximum number of samples to use in the Monte Carlo integration of every conditional expectation. For approach="ctree", n_MC_samples corresponds to the number of samples from the leaf node (see an exception related to the ctree.sample argument setup_approach.ctree()). For approach="empirical", n_MC_samples is the K parameter in equations (14-15) of Aas et al. (2021), i.e. the maximum number of observations (with largest weights) that is used, see also the empirical.eta argument setup_approach.empirical().

Details

For undocumented arguments, see setup_approach.categorical().

Author(s)

Lars Henry Berge Olsen

shapr documentation built on April 4, 2025, 12:18 a.m.