## 2 Materials and Methods  
This research focused on real data recorded during the process of producing radial tires. The measurements of the dimensions of tire components from the assembly stage (performed on a VMI MAXX tire assembly machine) and the results of the tire uniformity measurements taken during the final inspection stage (performed on a TUO machine) were recorded in dedicated databases. The authors emphasize that the examined relationships are non-linear; this is a conclusion drawn from the previously conducted EDA (exploratory data analysis), which is an analytical process focused on understanding the structure, patterns, properties, and hidden information in the collected data. This allowed for a better understanding of the essence of the data, identifying trends, detecting anomalies, and preparing the data for further analysis. The quality of the analyzed data was improved to make it easier to extract useful knowledge from them. Inaccuracies in the data were corrected (data cleaning), normalization was performed, and the size of the feature vector was reduced, leaving the most important ones (originally, the number of features was 396, which was reduced to 5). Data related to the production of one tire size were integrated and selected; they consisted of 6987 records for which 5 input variables were combined with 1 output variable for a given tire barcode. The data obtained were initially audited using Statistica 13 (StatSoft, Tulsa, OK, USA) software to detect and exclude uncertain, incomplete, or outlier data; in the case of data of debatable value, the decision to fix/remove the record was made by a panel of experts based on a statistical analysis. For all tested methods, the data (each attribute of the input and output vectors) were scaled to the range <0; 1>, because they should be in the same order of magnitude for a reliable comparison of the results. In this study, the data were divided into three sets: learning (70%), testing (15%), and validating (15%). A flow diagram of the registration and data preparation in the context of the tire production process is presented in Figure 2.  
Figure 2: Flow diagram of the registration and data preparation in the context of the tire production process. The tire components and features that most influence the model's prediction efficiency were selected due to the nature of the tire's operation and the specificity of the tire conicity phenomenon [9]. When in motion, the top tread (ensuring contact with the ground) and tread plies (ensuring the tire's stiffness) are mainly responsible for maintaining the set driving path in modern tire constructions [10]. Disturbing the symmetry of the aforementioned materials relative to the tire axis and failure to maintain the specification of their width are the main reasons why the conicity limit is exceeded. On this basis, the feature vector was reduced (396 features were reduced to 5). Moreover, this statement was further confirmed by the enterprise's years of experience in eliminating defects in a large number of tire sizes. Finally, the reasons described above (the physical properties of the tire and the company's experts' opinions) led to the selection of the following features (input variables):  
* BR1_WIDTH_MEASURED: tread ply 1's average total width;
* 1 BAP_BODY_AVG_OFF_CENTER: tread ply 1's average off-centeredness relative to the BT (band transfer) drum center line;
* BR2_WIDTH_MEASURED: tread ply 2's average total width;
* 2 BAP_BODY_AVG_OFF_CENTER: tread ply 2's average off-centeredness relative to the BT drum center line;
* TD_WIDTH_MEASURED: top tread average total width. One output variable was measured in compliance with [25] using a TUO (tire uniformity grading machine):  
* CON, conicity: This must be within the limit of X \(\pm\) 40 N, where X is a value (generally close to 0 N) determined on the basis of the average measurement from the first production batch of a new tire size. The volume of this population is determined by the customer's requirements (usually 1000-5000 tires).