test_predict_model

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 

test_predict_model function

<dl><dt>predict_model</dt>
<dd>Function.
The prediction function used when <code>model</code> is not natively supported.
See the documentation of <code>explain()</code> for details.</dd>
<dt>model</dt>
<dd>Objects.
The model object that ought to be explained.
See the documentation of <code>explain()</code> for details.</dd>
<dt>internal</dt>
<dd>List.
Holds all parameters, data, functions and computed objects used within <code>explain()</code>
The list contains one or more of the elements <code>parameters</code>, <code>data</code>, <code>objects</code>, <code>iter_list</code>, <code>timing_list</code>,
<code>main_timing_list</code>, <code>output</code>, and <code>iter_timing_list</code>.</dd></dl>

Arguments

Model testing function — test_predict_model

<dl>

<dt>predict_model</dt>
<dd>Function.
The prediction function used when <code>model</code> is not natively supported.
See the documentation of <code>explain()</code> for details.</dd>


<dt>model</dt>
<dd>Objects.
The model object that ought to be explained.
See the documentation of <code>explain()</code> for details.</dd>


<dt>internal</dt>
<dd>List.
Holds all parameters, data, functions and computed objects used within <code>explain()</code>
The list contains one or more of the elements <code>parameters</code>, <code>data</code>, <code>objects</code>, <code>iter_list</code>, <code>timing_list</code>,
<code>main_timing_list</code>, <code>output</code>, and <code>iter_timing_list</code>.</dd>

</dl>

Model testing function

test_predict_model: Model testing function

Description

Usage

Arguments