inv_gaussian_transform

Transforms new data to a standardized normal distribution

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 

inv_gaussian_transform function

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

Arguments

Author

Transforms new data to a standardized normal distribution — inv_gaussian_transform

<dl>

<dt>zx</dt>
<dd>Numeric vector. The first <code>n_z</code> items are the Gaussian data, and the last part is
the data with the original transformation.</dd>


<dt>n_z</dt>
<dd>Positive integer. Number of elements of <code>zx</code> that belongs to new data.</dd>

</dl>

Transforms new data to a standardized normal distribution

inv_gaussian_transform: Transforms new data to a standardized normal distribution

Description

Usage

Value

Arguments

Author