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数值特征工程中的四种缩放方法:原理、适用场景与局限性
数值特征工程是机器学习模型训练中不可跳过的预处理环节。处理数值数据时需要面对两个核心问题:特征的量级差异和异常值。以年龄和薪资为例,两者的数值范围差了好几个数量级,如果不做任何处理模型很可能仅凭数值大小就给薪资分配更高的权重,完全忽略年龄的作用。
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