## 1 Introduction
### Previously Research (Comparing ML Models)  
The task of comparing machine learning (ML) models for predictive performance poses significant methodological challenges that extend beyond straightforward accuracy comparisons. [PERSON]'s seminal work, Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms, underscores the risks associated with naive model comparisons that rely solely on performance metrics, such as accuracy, without accounting for statistical variability introduced by dataset partitioning [18]. Indeed, random splits of data into training and test subsets often produce inconsistent and unreliable results, potentially undermining the validity of any claims regarding model superiority. To address this concern, [PERSON] proposed robust statistical tests--most notably the use of k-fold cross-validation in conjunction with corrected resampled \(t\)-tests--to mitigate the dependencies introduced when the same data is reused in multiple folds. Such techniques are particularly pertinent when datasets are small or imbalanced, as they help minimize Type I and II errors, thereby increasing confidence in the reliability of observed differences in model performance [18]. [PERSON] and colleagues highlighted an additional complexity in their work, No Unbiased Estimator of the Variance of K-Fold Cross-Validation, which established that cross-validation lacks an unbiased estimator of variance [19]. While k-fold cross-validation is widely considered a gold standard for balancing bias and variance, the absence of an unbiased variance estimator complicates the interpretation of confidence intervals in comparative studies. Researchers, therefore, need to exercise caution in drawing strong conclusions from observed performance gaps between models, as even small discrepancies in variance estimation can skew significance tests [19]. In their 2003 paper [20], [PERSON] and [PERSON] introduced the corrected resampled \(t\)-test, an enhancement over the traditional \(t\)-test for comparing machine learning algorithms. This test adjusts for the increased Type I error rates caused by the overlap in training sets during cross-validation. By incorporating a correction factor that accounts for the correlation between sample estimates, the corrected resampled \(t\)-test offers more reliable performance assessments compared to earlier methods, such as those discussed by [PERSON] in 1998. The corrected resampled \(t\)-test is particularly beneficial in scenarios where training sets overlap, as it provides a more accurate estimation of variance by considering the dependencies introduced by such overlaps. This leads to more dependable hypothesis testing when evaluating and comparing the performance of machine learning models. Furthermore, [PERSON] and [PERSON] (2004) present a Repeated k-Fold Cross-Validation Correction formula that refines the variance estimates encountered in repeated runs of k-fold cross-validation [21]. Their methodology systematically averages performance across multiple folds and repetitions, thereby reducing the sampling fluctuations that often inflate or deflate apparent differences between competing models. By incorporating additional repetitions, this correction dampens the random effects introduced by the partitioning process, delivering tighter confidence intervals and more reliable hypothesis testing. Beyond these foundational contributions, other scholars have reinforced the importance of robust statistical methodologies for comparing ML models. [PERSON]'s seminal paper on statistical comparisons of multiple classifiers over multiple datasets detailed the pitfalls of inadequate significance testing protocols and recommended using proper non-parametric tests, such as the Friedman test or the aligned Friedman test, when comparing more than two models [22].