mahalanobis_distance_cpp

List of vectors indicating all factor combinations that should be included in the computations. Assumes that the first one is empty.

featureList

Xtrain_mat

Xtest_mat

Matrix. The Sigma-matrix in the Mahalanobis distance formula (<code>stats::cov(Xtrain_mat)</code>) gives Mahalanobis distance,
<code>diag(m)</code> gives the Euclidean distance.

mcov

S_scale_dist

Used to get the Euclidean distance as well by setting <code>mcov</code> = <code>diag(m)</code>.

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 

mahalanobis_distance_cpp function

(Generalized) Mahalanobis distance — mahalanobis_distance_cpp

(Generalized) Mahalanobis distance

mahalanobis_distance_cpp: (Generalized) Mahalanobis distance

Description

Usage

Arguments

Value