apply_dummies

Apply dummy variables - this is an internal function intended only to be used in
predict_model.xgb.Booster()

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 Løland (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

Annabelle Redelmeier

Anders Løland

Jens Christian Wahl

Camilla Lingjærde

Norsk Regnesentral 

apply_dummies function

<dl><dt>feature_list</dt>
<dd>List. The <code>feature_list</code> object in the output object after running
<code>make_dummies</code></dd>
<dt>testdata</dt>
<dd>data.table or data.frame. New data that has the same
feature names, types, and levels as <code>feature_list</code>.</dd></dl>

Arguments

Annabelle Redelmeier, Martin Jullum

Author

Apply dummy variables - this is an internal function intended only to be used in
predict_model.xgb.Booster() — apply_dummies

<dl>

<dt>feature_list</dt>
<dd>List. The <code>feature_list</code> object in the output object after running
<code>make_dummies</code></dd>


<dt>testdata</dt>
<dd>data.table or data.frame. New data that has the same
feature names, types, and levels as <code>feature_list</code>.</dd>

</dl>

apply_dummies: Apply dummy variables - this is an internal function intended only to be used in predict_model.xgb.Booster()

Description

Usage

Value

Arguments

Author