correction_matrix_cpp

tr_H

<p>numeric given the number of rows in H</p>

<p>correction term with trace_input in AICc formula</p>

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 L<c3><b8>land (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

Anders L<c3><b8>land

Jens Christian Wahl

Camilla Lingj<c3><a6>rde

Norsk Regnesentral 

correction_matrix_cpp function

correction term with trace_input in AICc formula — correction_matrix_cpp

correction term with trace_input in AICc formula

correction_matrix_cpp: correction term with trace_input in AICc formula

Description

Usage

Arguments

Value