aicc_full_single_cpp

mcov

logical indicating whether the Mahalanobis distance should be scaled with the number of variables

S_scale_dist

numeric specifying the scaling (sigma)

vector with the "response variable"

Temp-function for computing the full AICc with several X's etc

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

aicc_full_single_cpp: Temp-function for computing the full AICc with several X's etc

Description

Usage

Arguments

Value