shapviz.mid

<code>shapviz.mid()</code> is an S3 method for the <code>shapviz::shapviz()</code> generic, which calculates MID-derived Shapley values from a fitted MID model.

The goal of 'midr' is to provide a model-agnostic method for interpreting and explaining black-box predictive models by creating a globally interpretable surrogate model. The package implements 'Maximum Interpretation Decomposition' (MID), a functional decomposition technique that finds an optimal additive approximation of the original model. This approximation is achieved by minimizing the squared error between the predictions of the black-box model and the surrogate model. The theoretical foundations of MID are described in Iwasawa & Matsumori (2025) [Forthcoming], and the package itself is detailed in Asashiba et al. (2025) <doi:10.48550/arXiv.2506.08338>.

Ryoichi Asasihba

midr

Learning from Black-Box Models by Maximum Interpretation
Decomposition

Ryoichi Asashiba

Hirokazu Iwasawa

Reiji Kozuma

shapviz.mid function

<dl><dt>object</dt>
<dd>a "mid" object.</dd>
<dt>data</dt>
<dd>a data frame containing the observations for which to calculate MID-derived Shapley values. If not passed, data is automatically extracted based on the function call.</dd></dl>

Arguments

Calculate MID-Derived Shapley Values — shapviz.mid

<dl>

<dt>object</dt>
<dd>a "mid" object.</dd>


<dt>data</dt>
<dd>a data frame containing the observations for which to calculate MID-derived Shapley values. If not passed, data is automatically extracted based on the function call.</dd>

</dl>

shapviz.mid: Calculate MID-Derived Shapley Values

Description

Usage

Value

Arguments

Details