Abstract
Decision trees are one of the computing intelligence methods which proved to be very reliable as far as solving complicated multidimensional problems is concerned. Therefore, these methods are often used for extracting rules and to predict variables, what makes them useful for production automation. In this paper authors discuss the possibility of the use of decision trees for electric arc steelmaking process. The main goal is to predict temperature in the electric arc furnace by the use of decision trees. Proper automatic temperature prediction may reduce the number of temperature measurements during the process and consequently, it may shorten the time of the process. Optimization of production processes leads to real benefits, which is, for example, lowering costs of production. Calculations were done by the use of six types of regression decisions trees available in Statistica Dаtа Mіnеr software. The algorithms were examined considering the minimum error rate of temperature prediction, but also less complicated tree structure. The structure of a decision tree is also important owing to computational complexity.