Decision Trees Regularization Based on the Stability Principle

摘要

In this work, we consider a new approach to the construction of binary decision trees that provides the least error rate on independent samples. We suggest a concept of criterion-function regularization, which prevents superfluous fitting to the training sample. Conclusions reached in the article are confirmed by numerous experiments.