## 4 Discussion
### Future Research  
Null-hypothesis significance tests (NHST), used in this paper, highlight critical challenges in interpreting classifier comparisons. NHST frameworks, such as the signed-rank and correlated \(t\)-tests, often conflate statistical significance with practical relevance. For instance, \(p\)-values--central to NHST--are influenced by both effect size and sample size, which can lead to rejecting the null hypothesis even for negligible differences between classifiers when datasets are sufficiently large [46; 47; 48]. Moreover, NHST cannot affirmatively support the null hypothesis; it merely fails to reject it, leaving ambiguity about whether classifiers are truly equivalent [49]. These shortcomings underscore the need for alternative methodologies that provide more nuanced insights into classifier performance. Future research could address these limitations by adopting Bayesian approaches, which enable probabilistic interpretations of effect sizes and explicitly incorporate regions of practical equivalence (ROPE) to distinguish meaningful differences from trivial ones. A promising direction lies in the Bayesian hierarchical modeling framework proposed by [PERSON] et al. (2017) [24], which overcomes key NHST constraints. Unlike traditional NHST, their approach allows simultaneous inference on both dataset-specific effects (\(\delta\)i) and the population-level effect (\(\delta_{0}\)), offering a holistic view of classifier performance across multiple datasets. By leveraging Bayesian principles, researchers can quantify evidence for or against hypotheses, rather than relying on binary rejections of the null. For example, the Bayesian correlated \(t\)-test [50] computes posterior distributions of effect sizes, enabling direct probability statements about classifier superiority, equivalence, or practical insignificance. Extending such methods to hierarchical models--as in [PERSON] et al. (2017)--would further enhance generalizability by pooling information across datasets while accounting for variability. Future studies adopting this framework could yield more robust, interpretable conclusions, aligning statistical outcomes with practical relevance in classifier evaluation [24]. In addition to the methodological innovations, an equally important avenue for future research is to broaden the scope of the data sources examined. Although the present study focuses on a single dataset from the Community Innovation Survey (CIS2014) with a particular focus on Croatia, the Community Innovation Survey itself covers a wide range of European countries and sectors, each surveyed according to harmonized guidelines. This provides valuable opportunities for cross-country analyses comparing how machine learning models work on innovation data in different institutional or economic contexts. Similarly, a breakdown by industry--using standardized classifications such as NACE codes--could show whether certain algorithmic approaches have higher predictive power in specific sectors characterized by different rates of technological adoption or market competition. The inclusion of additional data from Eurostat or other official statistical agencies would thus facilitate more comprehensive tests of the generalizability of the models and allow researchers to determine whether the observed performance trends persist across different regions or industries. These extensions, in conjunction with the Bayesian approaches mentioned above, have the potential to form a comprehensive framework that evaluates and compares machine learning classifiers in a setting that extends well beyond the boundaries of a single region or dataset. It should be noted that the present work focuses on the study of performance metrics and relative computation times using medium-sized data. While the observed patterns give a reasonable indication of how these algorithms might behave, real large environments may have other nuances, such as higher memory usage, network overhead in distributed environments, or nonlinear scalability.