TITLE: Sample selection from a given dataset to validate machine learning models 

ABSTRACT: The selection of a validation basis from a full dataset is often required in
industrial use of supervised machine learning algorithm. This validation basis
will serve to realize an independent evaluation of the machine learning model.
To select this basis, we propose to adopt a "design of experiments" point of
view, by using statistical criteria. We show that the "support points" concept,
based on Maximum Mean Discrepancy criteria, is particularly relevant. An
industrial test case from the company EDF illustrates the practical interest of
the methodology.
The selection of a validation basis from a full dataset is often required in
industrial use of supervised machine learning algorithm. This validation basis
will serve to realize an independent evaluation of the machine learning model.
To select this basis, we propose to adopt a "design of experiments" point of
view, by using statistical criteria. We show that the "support points" concept,
based on Maximum Mean Discrepancy criteria, is particularly relevant. An
industrial test case from the company EDF illustrates the practical interest of
the methodology.
Sample selection from a given dataset to validate machine learning models
http://arxiv.org/abs/2104.14401v1