- Journal Details
- Format
- Journal
- eISSN
- 2083-4608
- First Published
- 26 Feb 2008
- Publication timeframe
- 4 times per year
- Languages
- English
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
- random forest
- predictor
- node
- feature
- test
Thermal resistance of concrete airfield pavements Selected Aspects of Modelling RFID Systems in Supply Chains APEKS 2 as a Modification of the APEKS Method Application supporting the design of parachute geometry for the sounding rocket recovery system The Use of Thermography in Preliminary Research on Temperature of Burnishing Influence of Graphite as a Filler on Selected Mechanical and Structural Properties of Layered Composites with Polyester-Glass Recyclate Research and Comparative Analysis of the Accuracy in Determining the Parameters of the Position of Aircraft by Air Traffic Control Radars Avia-W and GCA-2000 Assessment of the Impact of Flight Operations on Noise in the Vicinity of an Airport – Preliminary Study The Use of the Ant Algorithm in the Model of Safety Management of the Traffic Organization At the Apron Method of Increasing Reliability of Large Dimensional Bridge-Type Structures