Class imbalance is a problem of crucial challenge in many real-world machine learning applications. Traditional machine learning algorithms are likely to produce good accuracy scores on such datasets due to an obvious bias towards the majority class. Thus, accuracy as a measure of performance for algorithms working on imbalanced data is not very clearly defined since the classifier has poor predictive accuracy over the minority class. While previous work has used several resampling techniques to aid in improving the predictive accuracy of the minority class, in this study, we explore and compare the effectiveness of the Synthetic Minority Oversampling and Random Oversampling techniques over multiple learning algorithms and resampling ratios for eight different performance measures against two datasets from diverse domains such as medicine and engineering. The results of this study show that the effectiveness of these resampling techniques is a multivariate function relative to both the learning algorithms and the resampling ratios, as well as the coherent characteristics of datasets. The choice of performance measures to evaluate models built using these resampling techniques also vary, thus giving us more relevant information useful for future research and applications.