check_categorical_valid_MCsamp

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

internal

Complex machine learning models are often hard to interpret. However, in
many situations it is crucial to understand and explain why a model made a specific
prediction. Shapley values is the only method for such prediction explanation framework
with a solid theoretical foundation. Previously known methods for estimating the Shapley
values do, however, assume feature independence. This package implements methods which accounts for any feature
dependence, and thereby produces more accurate estimates of the true Shapley values.
An accompanying 'Python' wrapper ('shaprpy') is available through the GitHub repository.

Martin Jullum

shapr

Prediction Explanation with Dependence-Aware Shapley Values

Lars Henry Berge Olsen

Annabelle Redelmeier

Jon Lachmann

Nikolai Sellereite

Anders Løland

Jens Christian Wahl

Camilla Lingjærde

Norsk Regnesentral 

check_categorical_valid_MCsamp function

<dl><dt>dt</dt>
<dd>Data.table containing the generated MC samples (and conditional values) after each sampling step</dd>
<dt>n_MC_samples</dt>
<dd>Positive integer.
For most approaches, it indicates the maximum number of samples to use in the Monte Carlo integration
of every conditional expectation.
For <code>approach="ctree"</code>, <code>n_MC_samples</code> corresponds to the number of samples
from the leaf node (see an exception related to the <code>ctree.sample</code> argument <code>setup_approach.ctree()</code>).
For <code>approach="empirical"</code>, <code>n_MC_samples</code> 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
<code>empirical.eta</code> argument <code>setup_approach.empirical()</code>.</dd></dl>

Arguments

Author

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

<dl>

<dt>dt</dt>
<dd>Data.table containing the generated MC samples (and conditional values) after each sampling step</dd>


<dt>n_MC_samples</dt>
<dd>Positive integer.
For most approaches, it indicates the maximum number of samples to use in the Monte Carlo integration
of every conditional expectation.
For <code>approach="ctree"</code>, <code>n_MC_samples</code> corresponds to the number of samples
from the leaf node (see an exception related to the <code>ctree.sample</code> argument <code>setup_approach.ctree()</code>).
For <code>approach="empirical"</code>, <code>n_MC_samples</code> 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
<code>empirical.eta</code> argument <code>setup_approach.empirical()</code>.</dd>

</dl>

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

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

Description

Usage

Arguments

Author

Details