Abstract
Abstract The article in the introduction presents a brief description of the decision tree, and the purpose of the article was defined. Then, the process of building boosting trees was characterized, paying attention to the algorithm of their building. A method of building boosting trees for UZRGM fuzes is described. The assortment of fuzes in which this type of fuze is used is indicated, and the individual features of the fuze are presented, which are checked during laboratory diagnostic tests. The importance classes that were used to classify the revealed inconsistencies were also described. A boosting classification tree for UZRGM fuzes was designed and built. An exemplary graph of the built tree and its structure and also a fragment of specific values predicted in individual analyzed classes are shown. The matrix of incorrect classifications was determined, which determines the accuracy of the incorrect predictions. On selected examples of the analyzed classes, the designed model was assessed on the basis of the lift chart and gains chart.
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