A Software Interface for Machine Learning Model Applications
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This paper presents an innovative software interface for the utilization of widely used Machine Learning (ML) algorithms in a unified Python/R programming environment. A novel software model, the Rbox+, is proposed to execute ML algorithms by jointly leveraging the capabilities of the Python and R programming languages. Furthermore, more comprehensive and specialized architecture is made available for integrating ML into enterprise information systems. Unlike conventional ML Application Programming Interfaces (APIs) or isolated Enterprise Resource Planning (ERP) analytics tools, the Rbox+ enables transparent, language-independent execution and validation of ML models while exposing the underlying source code. The proposed approach supports practical applications in enterprise analytics, reproducible research, and enhancing interoperability between ERP systems, analytics platforms, and statistical programming environments. The proposed API has been tested and evaluated using a publicly available benchmark dataset for regression analysis, applying multiple ML models and comparative performance metrics. The obtained results demonstrate improved computational efficiency and scalability.
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