sample_combinations

Helper function to sample a combination of training and testing rows, which does not risk
getting the same observation twice. Need to improve this help file.

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 

sample_combinations function

<dl><dt>ntrain</dt>
<dd>Positive integer. Number of training observations to sample from.</dd>
<dt>ntest</dt>
<dd>Positive integer. Number of test observations to sample from.</dd>
<dt>nsamples</dt>
<dd>Positive integer. Number of samples.</dd>
<dt>joint_sampling</dt>
<dd>Logical. Indicates whether train- and test data should be sampled
separately or in a joint sampling space. If they are sampled separately (which typically
would be used when optimizing more than one distribution at once) we sample with replacement
if <code>nsamples &gt; ntrain</code>. Note that this solution is not optimal. Be careful if you're
doing optimization over every test observation when <code>nsamples &gt; ntrain</code>.</dd></dl>

Arguments

Author

Helper function to sample a combination of training and testing rows, which does not risk
getting the same observation twice. Need to improve this help file. — sample_combinations

<dl>

<dt>ntrain</dt>
<dd>Positive integer. Number of training observations to sample from.</dd>


<dt>ntest</dt>
<dd>Positive integer. Number of test observations to sample from.</dd>


<dt>nsamples</dt>
<dd>Positive integer. Number of samples.</dd>


<dt>joint_sampling</dt>
<dd>Logical. Indicates whether train- and test data should be sampled
separately or in a joint sampling space. If they are sampled separately (which typically
would be used when optimizing more than one distribution at once) we sample with replacement
if <code>nsamples &gt; ntrain</code>. Note that this solution is not optimal. Be careful if you're
doing optimization over every test observation when <code>nsamples &gt; ntrain</code>.</dd>

</dl>

Helper function to sample a combination of training and testing rows, which does not risk
getting the same observation twice. Need to improve this help file.

sample_combinations: Helper function to sample a combination of training and testing rows, which does not risk getting the same observation twice. Need to improve this help file.

Description

Usage

Value

Arguments

Author