Decision Trees in the Tasks of Human Prediction

Predictive psychodiagnostics is the process of forming a predictive assessment of any metric based on the data of psychodiagnostic testing. The formation of a predictive estimate is a classification problem and a variety of algorithmic approaches can be used to solve it – logistic regression, support vector machines, decision trees, neural networks, and many others. The article describes the practice of using algorithms based on decision trees to create models used in solving predictive psychodiagnostics problems, as well as cases in which their application is most relevant. The results obtained using the models built on the basis of the decision tree algorithm using gradient boosting show sufficient predictability and statistical significance in comparison with the methods of traditional psychodiagnostics used to solve personnel assessment problems