Error - Quality Correlation

From The Foundation for Best Practices in Machine Learning
Technical Best Practices > Data Quality > Error - Quality Correlation

Error - Quality Correlation


Document and assess whether low-quality datapoints (those with low-confidence, uncertain, nonsensical, missing and/or imputed attributes) correlate with high (rates of) error, and how this affects (Sub)populations. If so, take additional measures to increase data quality and/or improve Model performance for specific (Sub)populations.


To (a) prevent introducing bias to Model Outcomes due to low quality data; (b) whether the Model dataset(s) quality is sufficient for Product Definition(s); and (c) highlight associated risks that might occur in the Product Lifecycle.

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