hat_matrix_cpp

mcov

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

S_scale_dist

numeric specifying the scaling (sigma)

Computing single H matrix in AICc-function using the Mahalanobis distance

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

hat_matrix_cpp: Computing single H matrix in AICc-function using the Mahalanobis distance

Description

Usage

Arguments

Value