KMeansSMOTE is a technique used to address the problem of class imbalance in datasets, particularly in machine learning. It combines the K-Means clustering algorithm with SMOTE (Synthetic Minority Over-sampling Technique) to generate synthetic samples for the minority class. By clustering the minority class instances, it creates new samples that are more representative of the underlying data distribution. This method enhances the performance of classifiers by providing a more balanced dataset, which can lead to improved accuracy and generalization. KMeansSMOTE is particularly useful in scenarios where the minority class is significantly underrepresented, helping to mitigate bias in predictive modeling.