  The study of the classifier's design and it's usage is one of the most
important machine learning areas. With the development of automatic machine
learning methods, various approaches are used to build a robust classifier
model. Due to some difficult implementation and customization complexity,
genetic programming (GP) methods are not often used to construct classifiers.
GP classifiers have several limitations and disadvantages. However, the concept
of "soft" genetic programming (SGP) has been developed, which allows the
logical operator tree to be more flexible and find dependencies in datasets,
which gives promising results in most cases. This article discusses a method
for constructing binary classifiers using the SGP technique. The test results
are presented. Source code - https://github.com/survexman/sgp_classifier.
