How does data quality affect the performance of AI models?
Data quality is crucial for AI models as it directly influences their accuracy and reliability. Poor quality data can lead to biased or incorrect predictions.
Can you give me examples of poor data quality?
Sure! Examples include missing values, incorrect labels, duplicate entries, and inconsistent formats. Each of these can skew model training.
What can be done to improve data quality?
Improving data quality can involve data cleaning, validation, and using robust data collection methods. Regular audits can also help.
How do I measure the impact of data quality on my model?
You can measure impact by comparing model performance metrics, like accuracy and F1 score, before and after data quality improvements.
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