Predictive data mining models in the tests of propelling charges

Abstract

In the article, in the introduction, the concept of predictive data mining models and was defined and the purpose of the article was specified. Then, the method of building predic-tive models was characterized and the elements of ammunition were indicated, the test results of which were prepared for the building of models, and the types of ammunition in which the propellant charge is present were indicated. The results of building four data mining models are presented. Predictive models for C&RT, CHAID and exhaustive CHAID decision trees were designed and built. The fourth model analyzed was the SANN model, i.e. the model of neural networks. For each of the tree models, a schema of the designed tree, the rate of false predictions and the parameters of goodness of fit of the built models are shown. For the SANN model, the parameters of the selected neural network were addi-tionally characterized. An analysis of the built models was made and, based on the ob-tained results, the best designed predictive data mining model was indicated. At the end, the graphical form of the workspace predefined by the GC Advanced Comprehensive Classifiers project is shown.