Blind Performance Validation

From The Foundation for Best Practices in Machine Learning
Technical Best Practices > Performance Robustness > Blind Performance Validation

Blind Performance Validation

Control

Document and validate that Model Performance can always be reproduced on never-before-seen hold-out data-subsets and prove that these hold-out data-subsets are never used to guide Model and Product design choices by comparing Model performance on the hold-out dataset. If performance cannot be reproduced on never-before-seen hold-out data-subset, take measures to improve robustness and Model fitting as much as is reasonably practical.


Aim

To (a) ensure Model performance robustness against insufficient generalization capabilities on live data (such as overfitting); and (b) highlight associated risks that might occur in the Product Lifecycle.


Additional Information