Holistic Robust Data-Driven Decisions. (arXiv:2207.09560v1 [stat.ML])

The design of data-driven formulations for machine learning and
decision-making with good out-of-sample performance is a key challenge. The
observation that good in-sample performance does not guarantee good
out-of-sample performance is generally known as overfitting. Practical
overfitting can typically not be attributed to a single cause but instead is
caused by several factors all at once. We consider here three overfitting
sources: (i) statistical error as a result of working with finite sample data,
(ii) data noise which occurs when …

arxiv data data-driven decisions ml