1. bookVolume 52 (2022): Issue 1 (March 2022)
Journal Details
License
Format
Journal
eISSN
2083-4608
First Published
26 Feb 2008
Publication timeframe
4 times per year
Languages
English
access type Open Access

Random Forest in the Tests of Small Caliber Ammunition

Published Online: 13 Apr 2022
Volume & Issue: Volume 52 (2022) - Issue 1 (March 2022)
Page range: 73 - 85
Journal Details
License
Format
Journal
eISSN
2083-4608
First Published
26 Feb 2008
Publication timeframe
4 times per year
Languages
English
Abstract

In the introduction of this article the method of building a random forest model is presented, which can be used for both classification and regression tasks. The process of designing the random forest module was characterized, paying attention to the classification tasks module, which was used to build the author’s model. Based on the test results, a random forest model was designed for 7,62 mm ammunition with T-45 tracer projectile. Predictors were specified and values of stop parameters and process stop formulas were determined, on the basis of which a random forest module was built. An analysis of the resulting random forest model was made in terms of assessing its prediction and risk assessment. Finally, the designed random forest model has been refined by adding another 50 trees to the model. The enlarged random forest model occurred to be slightly stronger and it should be implemented.

Keywords

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