lag_data

Lag a matrix of variables a specific number of lags for each variables.

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 

lag_data function

<dl><dt>x</dt>
<dd>The matrix of variables (one variable per column).</dd>
<dt>lags</dt>
<dd>A numeric vector denoting how many lags each variable should have.</dd></dl>

Arguments

Lag a matrix of variables a specific number of lags for each variables. — lag_data

<dl>

<dt>x</dt>
<dd>The matrix of variables (one variable per column).</dd>


<dt>lags</dt>
<dd>A numeric vector denoting how many lags each variable should have.</dd>

</dl>

Lag a matrix of variables a specific number of lags for each variables.

lag_data: Lag a matrix of variables a specific number of lags for each variables.

Description

Usage

Value

Arguments