Summary of Machine Learning Evaluation Metric Discrepancies Across Programming Languages and Their Components: Need For Standardization, by Mohammad R. Salmanpour et al.
Machine Learning Evaluation Metric Discrepancies across Programming Languages and Their Components: Need for Standardization
by Mohammad R. Salmanpour, Morteza Alizadeh, Ghazal Mousavi, Saba Sadeghi, Sajad Amiri, Mehrdad Oveisi, Arman Rahmim, Ilker Hacihaliloglu
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Software Engineering (cs.SE); Computational Physics (physics.comp-ph)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper evaluates a wide range of metrics for various machine learning (ML) tasks, including classification, regression, clustering, correlation analysis, statistical tests, segmentation, and image-to-image translation. The study compares these metrics across Python libraries, R packages, and Matlab functions to identify inconsistencies and highlight discrepancies. The findings emphasize the need for standardization in ML evaluation metrics to ensure reliable and reproducible evaluations across platforms. The paper examines various tasks, including binary classification, multi-class classification, regression, clustering, correlation analysis, statistical tests, 2D segmentation, 3D segmentation, 2D image-to-image translation, and 3D image-to-image translation. The results show that only some metrics are consistent across platforms, such as Accuracy, Balanced Accuracy, Cohens Kappa, F-beta Score, MCC, Geometric Mean, AUC, and Log Loss in binary classification. The study concludes that ML evaluation metrics require standardization and recommends using consistent metrics for different tasks to effectively compare ML techniques and solutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how we evaluate machine learning models. It’s like trying to grade a student’s work – we need to make sure everyone is using the same rules. The study compares different ways to measure how well a model works across different programming languages, like Python or R. They found that some measures are good for certain tasks, but not others. For example, one way to measure how well a classification model works might be good for binary classification (like 0s and 1s), but not as good for multi-class classification (like 0s, 1s, and 2s). The paper says that we need to standardize these measures so we can compare different models fairly. This will help us make better decisions about which model is best. |
Keywords
» Artificial intelligence » Auc » Classification » Clustering » Machine learning » Regression » Translation
