## 5 Conclusions  
In this work, multiple machine learning algorithms--both ensemble-based and single-model approaches--were applied to a firm-level innovation dataset to predict which companies would achieve higher innovation intensity. Tree-based boosting methods were found to perform consistently well on several metrics, notably accuracy, F1, precision, and area under the ROC curve, while a kernel-based method performed best on recall. This divergence suggests that no single algorithm dominates in all performance measures but that different algorithmic strengths emerge depending on the aspect of prediction accuracy being evaluated.  
Implementation details showed that models using boosting frameworks were slightly more resource intensive but provided superior discriminative power on many classification metrics, while simpler methods generally provided faster computations with moderate accuracy. Therefore, the pragmatic decision of which algorithm to use should be determined by a trade-off between computational effort and the relative importance of specific performance goals (e.g., maximizing sensitivity versus balancing overall accuracy).  
Methodologically, various statistical procedures were used to determine that significant performance differences exist between the compared models. Cross-validation procedures were combined with corrections for repeated use of the same data, which reduced the risk of inflated Type I error rates. Nevertheless, it was found that results can fluctuate if the variance arising from overlapping folds is not carefully accounted for. The interplay between the size of the training set, the repetition strategies, and the complexity of the model also highlighted the importance of robust experimental designs that take into account computational cost, sample size, and methodological rigor.  
Although one class of ensemble algorithms was found to be particularly reliable, it is possible that domain-specific variations in data structure or class distribution could favor other techniques. Future analyzes that integrate Bayesian methods, hierarchical modeling, and additional qualitative insights into firms' innovation processes could provide even more accurate or interpretable predictions. By systematically fitting models to both data features and desired performance criteria, stakeholders can optimize resource allocation and maximize the potential impact of machine learning in shaping innovation outcomes.  
Conceptualization, M.M. and D.P.; methodology, M.M. and K.D.; software, M.M. and K.D.; validation, M.M., K.D., and D.P.; formal analysis, M.M.; investigation, K.D.; resources, D.P.; data curation, K.D.; writing--original draft preparation, M.M.; writing--review and editing, K.D. and D.P.; visualization, M.M. and D.P.; supervision, D.P.; project administration, D.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data that support the findings of this study are subject to restrictions and are not publicly available due to confidentiality and legal agreements. The data used in this research are third-party data from the Community Innovation Survey (CIS) 2014,managed by Eurostat. Access to these data can be requested directly from Eurostat or relevant national statistical offices. Detailed information about the application process for accessing CIS microdata is available at [[https://ec.europa.eu/eurostat/web/microdata/community-innovation-survey](https://ec.europa.eu/eurostat/web/microdata/community-innovation-survey)]([https://ec.europa.eu/eurostat/web/microdata/community-innovation-survey](https://ec.europa.eu/eurostat/web/microdata/community-innovation-survey)) (accessed on 1 December 2024).  
**Conflicts of Interest:** The authors declare no conflict of interest.