For classification tasks, data imbalance is a common challenge. To address this issue, we utilize synthetic sampling methods, generating additional sampling points for the minority class or undersampling the majority classes. The SMOTE, SMOTENC, and SMOTEN algorithms are implemented based on whether the descriptors are non-categorical, both categorical and non-categorical, and only categorical, respectively. In cases of small datasets, we recommend oversampling the minority class instead of removing points from the majority class. A new data frame containing the synthetic points is created and used for the training and evaluation of the model, as described earlier. Additionally, the misclassified function from the workflow functions module can be used to identify data points that have been misclassified and interrogate the model further.