<![CDATA[Machine learning models are commonly evaluated using a single experimental run, implicitly assuming that performance remains stable under fixed conditions. However, many machine learning algorithms involve stochastic processes that may cause performance variability across different random seed configurations. We examine how sensitive tabular classifiers are to the choice of random seed. Multi-Layer Perceptron (MLP), Random Forest, Support Vector Machine (SVM), and XGBoost were each trained and tested under 30 seeds on three public benchmark datasets. To assess robustness, this study introduces two complementary stability measures: Stability Sensitivity Index (SSI) and Relative Robustness Score (RRS). Stability was measured on three metrics: Accuracy, F1-score, and ROC-AUC. The results demonstrate that model performance can vary substantially across random seed configurations; on the smallest dataset, the accuracy of a single run differed by up to 7.9 percentage points depending on the seed. XGBoost achieved the highest mean performance on the two large datasets and, in most cases, also the lowest variability there, whereas SVM was the most stable model on the small Breast Cancer dataset. MLP was generally the most seed-sensitive model on the large datasets, and Random Forest on the small one. F1-score varied considerably more than Accuracy and ROC-AUC, particularly under class imbalance. Furthermore, the smallest dataset generally exhibits greater variability than the larger datasets. These results argue for reporting seed-level variability, not only mean scores, as a routine part of model evaluation. The proposed SSI and RRS metrics can support stability-aware model selection and reproducibility-oriented evaluation in applications requiring consistent predictive performance.]]>
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