train_PEAXAI

This function trains a set of models and selects best hyperparameters for each of them.

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

train_PEAXAI function

<dl><dt>data</dt>
<dd>A <code>data.frame</code> or <code>matrix</code> containing the variables in the model.</dd>
<dt>method</dt>
<dd>Parameters for controlling the training process (from the <code>'caret'</code> package).</dd>
<dt>parameters</dt>
<dd>A <code>list</code> of selected machine learning models and their hyperparameters.</dd>
<dt>trControl</dt>
<dd>A <code>list</code> of selected machine learning learning.</dd>
<dt>metric_priority</dt>
<dd>A <code>string</code> specifying the summary metric for classification to select the optimal model. Default includes <code>"Balanced_Accuracy"</code> due to (normally) unbalanced data.</dd>
<dt>seed</dt>
<dd>Integer. Seed for reproducibility.</dd></dl>

Arguments

Training a Classification Machine Learning Model — train_PEAXAI

<dl>

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


<dt>method</dt>
<dd>Parameters for controlling the training process (from the <code>'caret'</code> package).</dd>


<dt>parameters</dt>
<dd>A <code>list</code> of selected machine learning models and their hyperparameters.</dd>


<dt>trControl</dt>
<dd>A <code>list</code> of selected machine learning learning.</dd>


<dt>metric_priority</dt>
<dd>A <code>string</code> specifying the summary metric for classification to select the optimal model. Default includes <code>"Balanced_Accuracy"</code> due to (normally) unbalanced data.</dd>


<dt>seed</dt>
<dd>Integer. Seed for reproducibility.</dd>

</dl>

train_PEAXAI: Training a Classification Machine Learning Model

Description

Usage

Value

Arguments