Probability calibration is commonly utilized to enhance the reliability and interpretability of probabilistic classifiers, yet its potential for reducing algorithmic bias remains under-explored. In this study, the role of probability calibration techniques in mitigating bias associated with sensitive attributes, specifically country of origin, within binary classification models is investigated. Using a real-world lead-generation 2853 × 8 matrix dataset characterized by substantial class imbalance, with the positive class representing 1.4% of observations, several binary classification models were evaluated and the best-performing model was selected as the baseline for further analysis. The evaluated models included Binary Logistic Regression with polynomial degrees of 1, 2, 3, and 4, Random Forest, and XGBoost classification algorithms. Three widely used calibration methods, Platt scaling, isotonic regression, and temperature scaling, were then used to assess their impact on both probabilistic accuracy and fairness metrics of the best-performing model. The findings suggest that post hoc calibration can effectively reduce the influence of sensitive features on predictions by improving fairness without compromising overall classification performance. This study demonstrates the practical value of incorporating calibration as a straightforward and effective fairness intervention within machine learning workflows.

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Transfer learning has advanced significantly in domains like computer vision and natural language processing, yet its application to tabular data remains challenging, with traditional models like XGBoost often outperforming deep learning approaches due to issues like variance instability and slow convergence. This study investigates the impact of weight initialization techniques on transfer learning efficacy during partial fine-tuning, hypothesizing that optimized methods enhance (i) variance stability (consistent activation and gradient magnitudes), (ii) convergence speed (faster loss reduction), and (iii) generalization (improved out-of-distribution accuracy). Established techniques including Xavier/Glorot, He/Kaiming, Orthogonal, and fan-averaged initializations are evaluated on modern foundation models like TabuLa-8B, using benchmarks such as OpenML and CC-18 (72 datasets). Additionally, Feature-Adaptive Variance Initialization (FAVI) is proposed, which adapts variances based on per-feature statistics, which is mathematically proven to preserve unit output variance. Empirical results demonstrate 15–20% improvements in convergence speed and 1.5–2.5% in generalization. Research contributions include a theoretical formulation of FAVI and insights advancing tabular data modeling.

This study examines the financial and operational sustainability of modernizing drainage pump station systems in Vojvodina, aiming to improve energy efficiency and adapt to climate change. Various technical and economic modernization scenarios were analyzed to identify the optimal variant (V3.1), which balances investment costs, operational efficiency, and long-term environmental benefits. The analytical approaches used include multicriteria analysis, the Analytical Hierarchy Process (AHP) model, the TOPSIS technique, and fuzzy logic, providing comprehensive insights into the ranking of the four proposed variants. The results indicate that Variant V3.1, which features local SCADA systems and centralized management, offers an optimal solution for modernization. This model enhances operational reliability, reduces energy costs, and contributes to the sustainable development of the region through more efficient resource utilization. The proposed approach not only meets the specific needs of Vojvodina but also provides a broader framework for assessing similar infrastructure projects in other regions.