Probabilistic Efficiency Analysis Using Explainable Artificial Intelligence

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Documentation for package ‘PEAXAI’ version 1.0.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.

Help Pages

convex_facets	Create New SMOTE Units to Balance Data combinations of m + s
data	Simulated efficiency dataset (100 DMUs)
find_beta_maxmin	Search Range for Directional Efficiency Parameter (beta)
firms	Spanish Food Industry Firms Dataset
get_SMOTE_DMUs	Create New SMOTE Units to Balance Data combinations of m + s
label_efficiency	Data preprocessing and efficiency labeling with Additive DEA
PEAXAI_fitting	Training Classification Models to Estimate Efficiency
PEAXAI_global_importance	Global feature importance for efficiency classifiers
PEAXAI_peer	Identify Benchmark Peers Based on Estimated Efficiency Probabilities
PEAXAI_predict	Predict Probability of Efficiency Using a Fitted Model
PEAXAI_ranking	Generate Efficiency Rankings Based on Probabilistic Classification
PEAXAI_targets	Projection-Based Efficiency Targets
preprocessing	Prepare Data and Handle Errors
SMOTE_data	Create New SMOTE Units to Balance Data combinations of m + s
train_PEAXAI	Training a Classification Machine Learning Model
xai_prepare_sets	Prepare Training and Target Datasets from a caret Model