## 1 Introduction
### Previously Research (Comparing ML Models)
Similarly, [PERSON] and [PERSON]'s examination of misclassification costs showed that performance metrics must be carefully contextualized, especially in real-world applications where class distributions and error costs vary [23]. These concerns resonate in industrial and innovation-focused research, where datasets are not only heterogeneous but also exhibit evolving characteristics that can influence the stability of predictive models. Furthermore, researchers have explored alternative corrective strategies to bolster the robustness of cross-validation procedures. In the paper Statistical Comparison of Classifiers through Bayesian Hierarchical Modelling [24], the authors propose a k-fold cross-validation correction grounded in Bayesian hierarchical models. This approach not only acknowledges the inherent variability introduced by partitioning the dataset multiple times but also integrates prior information about the distribution of performance metrics, potentially leading to more nuanced statistical inferences. By modeling the hierarchical structure of the data and explicitly accounting for the dependencies between folds, Bayesian hierarchical methods offer an enriched perspective on classifier comparisons, enabling researchers to place observed performance differences into a probabilistic framework that more accurately reflects underlying uncertainties. Together, these Bayesian and cross-validation correction techniques align with the overarching goal of producing statistically rigorous comparisons, echoing the emphasis on controlling Type I and II errors and reinforcing the cautionary stances advocated in earlier works. Through these refined frameworks, researchers gain a clearer understanding of when observed performance differentials signify genuine model superiority, as opposed to statistical artifacts arising from data reuse and overlapping partitions. In the realm of innovation studies, the predictive modeling of innovation outcomes poses additional complexities stemming from multifaceted data types--ranging from patent citations to R&D investments--and rapidly changing market conditions [25]. Priorinvestigations, including those drawing on OECD's Oslo Manual frameworks [26], have underscored how metrics of innovation performance can exhibit high variance and intricate interdependencies across firms, regions, and industries. Empirical studies on innovation predictive tasks often adopt ensemble techniques--such as XGBoost, CatBoost, and LightGBM--to capture non-linear relationships and interactions [3], further emphasizing the need for careful methodological rigor in model selection and evaluation. These diverse strands of research collectively highlight that valid comparisons among ML models, such as Logistic Regression, Random Forests, Support Vector Machines (SVMs), and ensemble methods, must be anchored in statistically sound experimental designs. By integrating robust statistical analysis tools, cross-validation protocols, corrected significance testing, and explicit acknowledgment of variance estimation challenges, researchers can produce more reliable and reproducible findings. Such an approach is indispensable for the present study's comparative analysis of ML models for predicting innovation outcomes, where methodological precision is crucial for drawing insights that are both theoretically sound and practically actionable.