This article presents the methodology for exploratory analysis of data from microstructural studies of compacted graphite iron to gain

knowledge about the factors favouring the formation of ausferrite. The studies led to the development of rules to evaluate the content of

ausferrite based on the chemical composition. Data mining methods have been used to generate regression models such as boosted trees,

random forest, and piecewise regression models. The development of a stepwise regression modelling process on the iteratively limited

sets enabled, on the one hand, the improvement of forecasting precision and, on the other, acquisition of deeper knowledge about the

ausferrite formation. Repeated examination of the significance of the effect of various factors in different regression models has allowed

identification of the most important variables influencing the ausferrite content in different ranges of the parameters variability.