## 2 Materials and Methods  
### Dataset  
The dataset employed in this study derives from the 2014 iteration of the Community Innovation Survey (CIS2014), conducted under the auspices of the European Commission [16]. Specifically, we utilized the Croatian sample of the survey, which initially comprised 10,165 weighted observations. Within this pool, 2275 observations were flagged as demonstrating innovation output (i.e., some form of innovation behavior), thereby constituting the primary population of interest. For analytical refinement, this subset of 2275 weighted observations was represented by 909 unique data entries, forming the target cohort for the study.  
Each company in the dataset was characterized by eight innovation-activity variables serving as inputs, along with a composite measure of innovation intensity as the output. The eight input variables--Internal R&D, External R&D, Acquisition of Equipment, Acquisition of Knowledge, Training, Market, and Design--were binarily encoded (0 or 1), thereby capturing the presence or absence of each specific innovation activity. The output variable, denoted as the \"innovation sum,\" was computed as the aggregated total of seven innovation outcomes--product innovation, service innovation, organizational innovation, marketing innovation, process innovation, ongoing innovation, and abandoned innovation--yielding integer values between 1 and 7.  
Given that the present research aims to compare the predictive performance of multiple classification-based machine learning models, a binary form of the innovation sum was ultimately adopted. Balancing the dataset involved the evaluation of various binning and thresholding strategies, including CART-based binning, median and mean splits, and quintile-based binning, which collectively indicated a balanced division threshold and suggested dropping entries with an innovation sum of 3.  
Entries were labeled as either \"Low Innovation\" (0) or \"High Innovation\" (1), depending on their aggregated innovation outcomes. To mitigate class imbalance and eliminate the need to maintain original sample weights during training, a resampling strategy was implemented, resulting in a balanced final dataset of 1696 observations. This balanced dataset comprised 855 entries categorized as Low Innovation (innovation sum \(\leq\) 2.0) and 867 entries categorized as High Innovation (innovation sum \(\geq\) 4.0); entries corresponding to an innovation sum of 3.0 were omitted to achieve a more balanced distribution. Consequently, each instance within this final dataset includes all eight input variables (in binary format) and the single binary output variable, reflecting the level of innovation intensity.  
Although the dataset is intrinsically binary, a standard scaling procedure was later applied to the input features to harmonize potential differences in variable scale and variance [27]. Additionally, to examine the dimensional structure of the input space, principal component analysis (PCA) retaining 95% of the variance was performed, with results indicating that all eight input variables collectively contributed to eight principal components. The relative explained variance of each component was 0.3325682, 0.13588163, 0.10990122, 0.10231265, 0.09203671, 0.08618338, 0.07657155, and 0.06454466, respectively, confirming that the original features adequately capture the underlying variance of the data [27].  
A comparative snapshot of the dataset before and after pre-processing is presented in Figure 1, illustrating the transformation following weight-based resampling, standard scaling, and PCA analysis.