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在数据分析和机器学习任务中，经常会遇到类别不平衡（Class Imbalance）的情况，即某些类别的样本数量远远少于其他类别的样本数量。这种情况可能会导致模型在预测时出现偏差，因为模型更倾向于预测出现频率更高的类别。在SAS环境中，如何有效地处理数据不平衡成为了一个重要的问题。
数据不平衡可能会导致以下问题：
