Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/3544 2018 2018 Faculty of Economics and Business Administration, Sofia University St. Kl. Ohridski, Sofia 1113, Bulgaria Ivan Ivan Faculty of Economics and Business Administration, Sofia University St. Kl. Ohridski, Sofia 1113, Bulgaria Borislava Toleva Faculty of Economics and Business Administration, Sofia University St. Kl. Ohridski, Sofia 1113, Bulgaria Ivan Ivanov The presented methodology provides an innovative way to answer a question that is rarely observed in academic literature: How can complex data issues like multiple class imbalance be solved using the available models in a simple and efficient way? In this approach, observations are modeled without additional preprocessing. Several classification models including Random Forest (RF), Support Vector Machines (SVM), and Decision Tree (DT) are utilized for conducting the classification analysis. The parameters of these models and the cross-validation function are adjusted to each individual set of observations. This approach has not been researched in depth. We test it about class imbalance in the target variable. Our results demonstrate the benefits of the proposed method.  First, parameter tuning of ML models can be an effective strategy to handle class imbalance. Second, random shuffling prior to cross validation can be a key to resolving the bias coming from multiclass imbalance. Another important finding is that the best results can be achieved when random shuffling, cross validation and parameter tuning are combined. These findings are key to handling class imbalance in classification. Therefore, this research extends the opportunities to handle class imbalance in a simple, quick, and effective way in cases without adding additional complexity to the model. 2025 2025 200 214 10.54216/FPA.180215 https://www.americaspg.com/articleinfo/3/show/3544