get_list_approaches

Integer vector. Note that
<code>length(n_features) &lt;= 2^m</code>, where <code>m</code> equals the number
of features.

n_features

Character vector of length <code>m</code>. All elements should be
either <code>"empirical"</code>, <code>"gaussian"</code> or <code>"copula"</code>.

approach

Helper function used in <code><a rd-options="" href="/link/explain.combined?package=shapr&version=0.1.3" data-mini-rdoc="shapr::explain.combined">explain.combined</a></code>

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 the method
described in Aas, Jullum and L<c3><b8>land (2019) <arXiv:1903.10464>, which accounts for any feature
dependence, and thereby produces more accurate estimates of the true Shapley values.

Martin Jullum

shapr

Prediction Explanation with Dependence-Aware Shapley Values

Nikolai Sellereite

Anders L<c3><b8>land

Jens Christian Wahl

Camilla Lingj<c3><a6>rde

Norsk Regnesentral 

get_list_approaches function

Helper function used in <code><a rd-options='' href='explain.combined'>explain.combined</a></code>

Helper function used in <code><a rd-options='' href='explain.combined'>explain.combined</a></code> — get_list_approaches

Helper function used in <code><a rd-options='' href='explain.combined'>explain.combined</a></code>

Helper function used in <code><a rd-options="" href="/link/explain.combined?package=shapr&version=0.1.3" data-mini-rdoc="shapr::explain.combined">explain.combined</a></code>

get_list_approaches: Helper function used in `explain.combined`

Description

Usage

Arguments

Value