gaussian_transform_separate

Numeric vector. The first <code>n_y</code> items is the data that is transformed, and last
part is the data with the original transformation.

Positive integer. Number of elements of <code>yx</code> that belongs to the gaussian data.

Transforms new data to standardized normal (dimension 1) based on other data transformations

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

gaussian_transform_separate: Transforms new data to standardized normal (dimension 1) based on other data transformations

Description

Usage

Arguments

Value