weighted.tabulate

<code>weighted.tabulate()</code> returns the sum of weights for each integer in the vector <code>bin</code>.

internal

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

weighted.tabulate function

<dl><dt>bin</dt>
<dd>a numeric vector of positive integers, or a factor.</dd>
<dt>w</dt>
<dd>a numeric vector of the sample weights for each value in <code>bin</code>.</dd>
<dt>nbins</dt>
<dd>the number of bins to be used.</dd></dl>

Arguments

Weighted Tabulation for Vectors — weighted.tabulate

<dl>

<dt>bin</dt>
<dd>a numeric vector of positive integers, or a factor.</dd>


<dt>w</dt>
<dd>a numeric vector of the sample weights for each value in <code>bin</code>.</dd>


<dt>nbins</dt>
<dd>the number of bins to be used.</dd>

</dl>

weighted.tabulate: Weighted Tabulation for Vectors

Description

Usage

Value

Arguments

Details

See Also