regression.check_sur_n_comb

Check that <code>regression.surrogate_n_comb</code> is either NULL or a valid integer.

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 

regression.check_sur_n_comb function

<dl><dt>regression.surrogate_n_comb</dt>
<dd>Positive integer.
Specifies the number of unique coalitions to apply to each training observation.
The default is the number of sampled coalitions in the present iteration.
Any integer between 1 and the default is allowed.
Larger values requires more memory, but may improve the surrogate model.
If the user sets a value lower than the maximum, we sample this amount of unique coalitions
separately for each training observations.
That is, on average, all coalitions should be equally trained.</dd>
<dt>n_coalitions</dt>
<dd>Integer. The number of used coalitions (including the empty and grand coalition).</dd></dl>

Arguments

Author

Check the regression.surrogate_n_comb parameter — regression.check_sur_n_comb

<dl>

<dt>regression.surrogate_n_comb</dt>
<dd>Positive integer.
Specifies the number of unique coalitions to apply to each training observation.
The default is the number of sampled coalitions in the present iteration.
Any integer between 1 and the default is allowed.
Larger values requires more memory, but may improve the surrogate model.
If the user sets a value lower than the maximum, we sample this amount of unique coalitions
separately for each training observations.
That is, on average, all coalitions should be equally trained.</dd>


<dt>n_coalitions</dt>
<dd>Integer. The number of used coalitions (including the empty and grand coalition).</dd>

</dl>

Check the <code>regression.surrogate_n_comb</code> parameter

regression.check_sur_n_comb: Check the `regression.surrogate_n_comb` parameter

Description

Usage

Arguments

Author