Abstract
This paper provides a comparative study of machine
learning techniques for two-group discrimination.
Simulated data is used to examine how the different
learning techniques perform with respect to certain
data distribution characteristics. Both linear and
nonlinear discrimination methods are considered. The
data has been previously used in the comparative
evaluation of a number of techniques and helps relate
our findings across a range of discrimination
techniques.
Users
Please
log in to take part in the discussion (add own reviews or comments).