Summary of Common Steps in Machine Learning Might Hinder the Explainability Aims in Medicine, by Ahmed M Salih
Common Steps in Machine Learning Might Hinder The Explainability Aims in Medicine
by Ahmed M Salih
First submitted to arxiv on: 30 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers explore the relationship between data pre-processing techniques used in machine learning and their impact on model explainability. Specifically, they examine common steps like handling missing values, outliers detection and removal, data augmentation, dimensionality reduction, data normalization, and confounding variable mitigation. While these techniques can improve model accuracy and reduce running time, they can also hinder model interpretability if not carefully considered, particularly in medical applications where understanding the decision-making process is crucial. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Data pre-processing is a crucial step in machine learning that helps models perform better and run faster. But did you know that some of these steps can actually make it harder to understand how the model makes decisions? This paper talks about common data pre-processing techniques and how they affect our ability to explain why the model made a certain prediction. |
Keywords
» Artificial intelligence » Data augmentation » Dimensionality reduction » Machine learning
