We just finished a quick “risk in optimization series.” Here at Win Vector we think this is a neglected topic that can easily be adapted to improve and even save optimization projects. Our series was: Modeling Uncertainty For Better Outcomes: here we argue for adopting “expectation minus k standard deviations” […]| Win Vector LLC
One of the bigger risks of iterative statistical or machine learning fitting procedures is over-fit or the dreaded data leak. Over-fit is when: a model performs better on training data than on future data. Some degree of over-fit is expected. A data leak is when: the model learns things about […]| Win Vector LLC