[logseq-plugin-git:commit] 2025-12-27T19:07:25.270Z

pages/PAPERS___SOSYM-MODELS-EXTENSION-ML-BENCHMARKING.md

@@ -94,7 +94,7 @@ priority:: [[P4]]
 				- The ==trade-off between accuracy and efficiency==. Some tools may have higher accuracy or precision than others, but also lower speed or scalability. On the other hand, some tools may be faster or more scalable than others, but also less accurate or precise. Therefore, it is important to balance between accuracy and efficiency when choosing or evaluating a tool.
 		- Conclusion
 			- Benchmarking ML tools is an essential step to assess their performance and suitability for different tasks and applications. However, benchmarking ML tools is not a simple process, as it involves many steps and challenges. In this blog post, we have elaborated on why benchmarking is needed when applying ML tools when automating some process; how typical benchmarking processes look like; and what are the issues when benchmarking ML tools.
-		- Machine learning (ML) is a fast-growing field with many applications in various domains and areas including MDE. Selecting the appropriate ML tool for a particular task can be difficult, as different ML tools may have different strengths and weaknesses. In this [[paper]], we presented  the \modelxglue framework, which we have designed to facilitate benchmarking ML [[MODELS]] specifically created to address MDE tasks. The framework has been designed to be able to manage different datasets, metrics, and execution environments.  The aim is to automate benchmarking processess [[by]] simplifying comparisong processess [[by]] limiting the burden related to the installation and management of the different artifacts that are typically involved. A catalogue of already available benchmarks has been presented and its execution has been presented.
+		- Machine learning (ML) is a fast-growing field with many applications in various domains and areas including MDE. Selecting the appropriate ML tool for a particular task can be difficult, as different ML tools may have different strengths and weaknesses. In this [[paper]], we presented  the \modelxglue framework, which we have designed to facilitate benchmarking ML [[MODELS]] specifically created to address MDE tasks. The framework has been designed to be able to manage different datasets, metrics, and execution environments.  The aim is to automate benchmarking processess [[by]] simplifying comparisong processess [[by]] limiting the burden related to the installation and management of the different artifacts that are typically involved. A catalogue of already available [[benchmarks]] has been presented and its execution has been presented.
 	- [[RelatedWork]]
 		- [[@Benchmarking Machine Learning Solutions in Production]]
 		- [[@PMLB: a large benchmark suite for machine learning evaluation and comparison]]