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Related Questions
- What are the key factors that contribute to data quality in the context of machine learning?
- How does data quality impact model accuracy, and what are the potential consequences of low-quality data?
- What is model bias, and how can it be introduced or exacerbated by poor data quality?
- What are some common data quality issues that can lead to biased or inaccurate models?
- How can data curation techniques, such as data cleaning and preprocessing, improve model accuracy and reduce bias?
- What role does data visualization play in identifying and addressing data quality issues?
- Can you provide examples of successful data curation strategies that have improved model performance and reduced bias in real-world applications?
- How does data curation fit into the broader machine learning lifecycle, and what are its key benefits and challenges?
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