weighted.mse

weighted.rmse

weighted.mae

weighted.medae

<code>weighted.mse()</code>, <code>weighted.rmse()</code>, <code>weighted.mae()</code> and <code>weighted.medae()</code> compute the loss based on the differences of two numeric vectors or deviations from the mean of a numeric vector.

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

Hirokazu Iwasawa

Reiji Kozuma

weighted.mse function

<dl><dt>x</dt>
<dd>a numeric vector.</dd>
<dt>y</dt>
<dd>an optional numeric vector. If passed, the loss is calculated for the differences between <code>x</code> and <code>y</code>. If not, the loss is calculated for the deviations of <code>x</code> from the weighted mean of itself.</dd>
<dt>w</dt>
<dd>a numeric vector of sample weights for each value in <code>x</code>.</dd>
<dt>...</dt>
<dd>optional parameters.</dd>
<dt>na.rm</dt>
<dd>logical. If <code>TRUE</code>, any <code>NA</code> and <code>NaN</code>s are removed from <code>x</code> before the calculation.</dd></dl>

Arguments

Weighted Loss Functions — weighted.mse

<dl>

<dt>x</dt>
<dd>a numeric vector.</dd>


<dt>y</dt>
<dd>an optional numeric vector. If passed, the loss is calculated for the differences between <code>x</code> and <code>y</code>. If not, the loss is calculated for the deviations of <code>x</code> from the weighted mean of itself.</dd>


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


<dt>...</dt>
<dd>optional parameters.</dd>


<dt>na.rm</dt>
<dd>logical. If <code>TRUE</code>, any <code>NA</code> and <code>NaN</code>s are removed from <code>x</code> before the calculation.</dd>

</dl>

weighted.mse: Weighted Loss Functions

Description

Usage

Value

Arguments

Details

Examples