sklearn.preprocessing 中standardscaler 和Normalizer 的区别 [英] Difference between standardscaler and Normalizer in sklearn.preprocessing
问题描述
sklearn.preprocessing 模块中的standardscaler 和normalizer 有什么区别?两者不做同样的事情吗?即使用偏差去除平均值和比例?
What is the difference between standardscaler and normalizer in sklearn.preprocessing module? Don't both do the same thing? i.e remove mean and scale using deviation?
推荐答案
来自 Normalizer 文档:
具有至少一个非零分量的每个样本(即数据矩阵的每一行)独立于其他样本重新缩放，使其范数(l1 或 l2)等于 1.
Each sample (i.e. each row of the data matrix) with at least one non zero component is rescaled independently of other samples so that its norm (l1 or l2) equals one.
通过去除均值和缩放到单位方差来标准化特征
Standardize features by removing the mean and scaling to unit variance
换句话说，Normalizer 按行作用，StandardScaler 按列作用.归一化器不会删除均值和按偏差缩放，而是将整行缩放到单位范数.
In other words Normalizer acts row-wise and StandardScaler column-wise. Normalizer does not remove the mean and scale by deviation but scales the whole row to unit norm.
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