create_ctree

Build all the conditional inference trees

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 

create_ctree function

<dl><dt>given_ind</dt>
<dd>Integer vector.
Indicates which features are conditioned on.</dd>
<dt>x_train</dt>
<dd>Data.table with training data.</dd>
<dt>use_partykit</dt>
<dd>String. In some semi-rare cases <code>party::ctree()</code> runs into an error related to the LINPACK
used by R. To get around this problem, one may fall back to using the newer (but slower) <code>partykit::ctree()</code>
function, which is a reimplementation of the same method. Setting this parameter to <code>"on_error"</code> (default)
falls back to <code>partykit::ctree()</code>, if <code>party::ctree()</code> fails. Other options are <code>"never"</code>, which always
uses <code>party::ctree()</code>, and <code>"always"</code>, which always uses <code>partykit::ctree()</code>. A warning message is
created whenever <code>partykit::ctree()</code> is used.</dd></dl>

Arguments

Annabelle Redelmeier, Martin Jullum

Author

Build all the conditional inference trees — create_ctree

<dl>

<dt>given_ind</dt>
<dd>Integer vector.
Indicates which features are conditioned on.</dd>


<dt>x_train</dt>
<dd>Data.table with training data.</dd>


<dt>use_partykit</dt>
<dd>String. In some semi-rare cases <code>party::ctree()</code> runs into an error related to the LINPACK
used by R. To get around this problem, one may fall back to using the newer (but slower) <code>partykit::ctree()</code>
function, which is a reimplementation of the same method. Setting this parameter to <code>"on_error"</code> (default)
falls back to <code>partykit::ctree()</code>, if <code>party::ctree()</code> fails. Other options are <code>"never"</code>, which always
uses <code>party::ctree()</code>, and <code>"always"</code>, which always uses <code>partykit::ctree()</code>. A warning message is
created whenever <code>partykit::ctree()</code> is used.</dd>

</dl>

create_ctree: Build all the conditional inference trees

Description

Usage

Value

Arguments

Author

Details