Summary of Impact Of Missing Values in Machine Learning: a Comprehensive Analysis, by Abu Fuad Ahmad et al.
Impact of Missing Values in Machine Learning: A Comprehensive Analysis
by Abu Fuad Ahmad, Md Shohel Sayeed, Khaznah Alshammari, Istiaque Ahmed
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 A novel investigation into the far-reaching consequences of missing values in machine learning (ML) workflows is presented in this study. The authors delve into the types, causes, and effects of missing values, highlighting the risks they pose to ML performance and generalization. The analysis focuses on the challenges posed by biased inferences, reduced predictive power, and increased computational burdens. Strategies for handling missing values, including imputation techniques and removal methods, are explored. Additionally, the impact of missing values on model evaluation metrics and cross-validation is examined. Case studies and real-world examples illustrate the practical implications of addressing missing values. The study concludes by emphasizing the need for ethical and transparent handling of missing values in ML models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how missing values affect machine learning (ML) models. It finds that missing values can make models less accurate, biased, or even unreliable. The authors explain why this is happening and suggest ways to fix it, like imputing the missing values or removing them altogether. They also show examples of real-world problems where missing values cause issues with ML model performance. |
Keywords
» Artificial intelligence » Generalization » Machine learning
