gaussian_transform_separate

Transforms new data to standardized normal (dimension 1) based on other data transformations

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 

gaussian_transform_separate function

<dl><dt>yx</dt>
<dd>Numeric vector. The first <code>n_y</code> items is the data that is transformed, and last
part is the data with the original transformation.</dd>
<dt>n_y</dt>
<dd>Positive integer. Number of elements of <code>yx</code> that belongs to the Gaussian data.</dd></dl>

Arguments

Author

Transforms new data to standardized normal (dimension 1) based on other data transformations — gaussian_transform_separate

<dl>

<dt>yx</dt>
<dd>Numeric vector. The first <code>n_y</code> items is the data that is transformed, and last
part is the data with the original transformation.</dd>


<dt>n_y</dt>
<dd>Positive integer. Number of elements of <code>yx</code> that belongs to the Gaussian data.</dd>

</dl>

Transforms new data to standardized normal (dimension 1) based on other data transformations

gaussian_transform_separate: Transforms new data to standardized normal (dimension 1) based on other data transformations

Description

Usage

Value

Arguments

Author