preprocessing

This function arranges the data in the required format and displays some error messages.

Provides a probabilistic framework that integrates Data Envelopment
Analysis (DEA) (Banker et al., 1984) <doi:10.1287/mnsc.30.9.1078> with machine
learning classifiers (Kuhn, 2008) <doi:10.18637/jss.v028.i05> to estimate both the
(in)efficiency status and the probability of efficiency for decision-making
units. The approach trains predictive models on DEA-derived efficiency labels
(Charnes et al., 1985) <doi:10.1016/0304-4076(85)90133-2>, enabling explainable
artificial intelligence (XAI) workflows with global and local interpretability
tools, including permutation importance (Molnar et al., 2018) <doi:10.21105/joss.00786>,
Shapley value explanations (Strumbelj & Kononenko, 2014) <doi:10.1007/s10115-013-0679-x>,
and sensitivity analysis (Cortez, 2011) <https://CRAN.R-project.org/package=rminer>.
The framework also supports probability-threshold peer selection and counterfactual
improvement recommendations for benchmarking and policy evaluation. The probabilistic
efficiency framework is detailed in González-Moyano et al. (2025)
"Probability-based Technical Efficiency Analysis through Machine Learning",
in review for publication.

Ricardo González Moyano

PEAXAI

Probabilistic Efficiency Analysis Using Explainable Artificial
Intelligence

Juan Aparicio

José Luis Zofío

Víctor España

preprocessing function

<dl><dt>data</dt>
<dd>A <code>data.frame</code> or <code>matrix</code> containing the variables in the model.</dd>
<dt>x</dt>
<dd>Column indexes of input variables in <code>data</code>.</dd>
<dt>y</dt>
<dd>Column indexes of output variables in <code>data</code>.</dd></dl>

Arguments

Prepare Data and Handle Errors — preprocessing

<dl>

<dt>data</dt>
<dd>A <code>data.frame</code> or <code>matrix</code> containing the variables in the model.</dd>


<dt>x</dt>
<dd>Column indexes of input variables in <code>data</code>.</dd>


<dt>y</dt>
<dd>Column indexes of output variables in <code>data</code>.</dd>

</dl>

preprocessing: Prepare Data and Handle Errors

Description

Usage

Value

Arguments