Asymmetric Error Weights

From The Foundation for Best Practices in Machine Learning

Asymmetric Error Weights


Document and assess whether Model errors, and error rates, are weighted asymmetrically in the Model.


To (a) ensure the adequate optimisation of Product Definitions through an assessment of the cost function and optimization procedure; (b) to respect the boundary conditions and requirements set by the Product Definitions; and (c) highlight associated risks that might occur in the Product Lifecycle

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