Data quality refers to the condition of a dataset with respect to factors such as accuracy, completeness, consistency, timeliness, and validity. High-quality data correctly represents the real-world construct it is intended to model and is fit for its intended use in decision-making or model training. In ML, poor data quality (e.g., mislabelled samples, noise, bias) can degrade model performance more than poor algorithm choice. Ensuring data quality may involve processes like validation rules (to catch out-of-range or illogical values), data cleaning, deduplication, and periodic audits. When combining datasets, maintaining consistent formats and definitions (data integrity) is key. Overall, trustworthy analytics and AI systems begin with high-quality, reliable data.
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