Who Wins the Race? (R Vs Python) - An Exploratory Study on Energy Consumption of Machine Learning Algorithms

• URL: http://arxiv.org/abs/2508.17344v1
• Date: Sun, 24 Aug 2025 12:59:47 GMT
• Title: Who Wins the Race? (R Vs Python) - An Exploratory Study on Energy Consumption of Machine Learning Algorithms
• Abstract summary: This paper aims to raise awareness of the energy costs associated with employing different programming languages for Machine Learning model training and inference.<n>Our study results reveal a statistically significant difference in costs between the two languages in 95% of the cases examined.<n>The choice of programming language can influence energy efficiency significantly, up to 99.16% during model training and up to 99.8% during inferences.
• Score: 5.294137281815185
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The utilization of Machine Learning (ML) in contemporary software systems is extensive and continually expanding. However, its usage is energy-intensive, contributing to increased carbon emissions and demanding significant resources. While numerous studies examine the performance and accuracy of ML, only a limited few focus on its environmental aspects, particularly energy consumption. In addition, despite emerging efforts to compare energy consumption across various programming languages for specific algorithms and tasks, there remains a gap specifically in comparing these languages for ML-based tasks. This paper aims to raise awareness of the energy costs associated with employing different programming languages for ML model training and inference. Through this empirical study, we measure and compare the energy consumption along with run-time performance of five regression and five classification tasks implemented in Python and R, the two most popular programming languages in this context. Our study results reveal a statistically significant difference in costs between the two languages in 95% of the cases examined. Furthermore, our analysis demonstrates that the choice of programming language can influence energy efficiency significantly, up to 99.16% during model training and up to 99.8% during inferences, for a given ML task.

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