Class imbalance poses a pervasive challenge in the field of machine learning, often leading to suboptimal performance of classification algorithms. In this study, we explore the efficacy of spline calibration to mitigate the impact of class imbalance on classifier predictions. Our research involves a systematic experimental comparison of various imbalanced ratios, ranging from mildly imbalanced to severely skewed datasets. We begin by introducing the problem of class imbalance in classification tasks and highlighting the importance of effective calibration techniques. Leveraging a diverse set of classification algorithms, we conduct experiments on carefully curated datasets with varying degrees of class imbalance. Our methodology encompasses data splitting, classifier training, and the application of spline calibration. The results of our experiments reveal intriguing insights into the performance of classification models before and after spline calibration across different imbalanced ratios. We observe notable improvements in calibration-aided models, particularly in terms of precision, recall, and area under the precision-recall curve (AUC-PR). Moreover, we analyze the influence of imbalanced ratios on calibration effectiveness, shedding light on when and how this technique can be most beneficial. In conclusion, this research demonstrates the value of spline calibration as a powerful tool for enhancing classification performance in the face of class imbalance. Our findings not only contribute to the growing body of knowledge on calibration methods but also provide practitioners with valuable guidance on selecting appropriate strategies for handling imbalanced datasets in classification tasks.