Data Veracity Uncertainty & Precision

From The Foundation for Best Practices in Machine Learning
Technical Best Practices > Data Quality > Data Veracity Uncertainty & Precision

Data Veracity Uncertainty & Precision


Document and assess the veracity and precision of data. If compromised, uncertain and/or unknown, document and assess (i) the causes and sources hereof and (ii) statistical accuracy .Incorporate appropriate statistical handling procedures, such as calibration, and appropriate control mechanisms in Model, or discard the data dimension.


To assess (a) the risk of low quality data introducing bias to Model data and/or outcomes; (b) a priori the plausibly achievable performance; (c) whether the Model dataset(s) quality is sufficient for Product Definitions; and (d) highlight associated risks that might occur in the Product Lifecycle.

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