Scikit-learn:preprocessing.scale() 与 preprocessing.StandardScalar() [英] Scikit-learn: preprocessing.scale() vs preprocessing.StandardScalar()

问题描述

我理解缩放意味着将均值(均值 = 0)居中并制作单位方差(方差 = 1).

I understand that scaling means centering the mean(mean=0) and making unit variance(variance=1).

但是，scikit-learn 中的 preprocessing.scale(x) 和 preprocessing.StandardScalar() 有什么区别?

But, What is the difference between preprocessing.scale(x)and preprocessing.StandardScalar() in scikit-learn?

推荐答案

那些做的完全一样，但是:

Those are doing exactly the same, but:

• preprocessing.scale(x) 只是一个函数，它转换一些数据
• preprocessing.StandardScaler() 是一个支持 Transformer API
的类

• preprocessing.scale(x) is just a function, which transforms some data
• preprocessing.StandardScaler() is a class supporting the Transformer API

我总是使用后者，即使我不需要 inverse_transform 和 co.StandardScaler() 支持.

I would always use the latter, even if i would not need inverse_transform and co. supported by StandardScaler().

摘自 文档:

函数 scale 提供了一种快速简便的方法来对单个类似数组的数据集执行此操作

The function scale provides a quick and easy way to perform this operation on a single array-like dataset

预处理模块进一步提供了一个实用程序类 StandardScaler，它实现了 Transformer API 来计算训练集的均值和标准差，以便以后能够在测试集上重新应用相同的变换.因此，此类适用于 sklearn.pipeline.Pipeline

The preprocessing module further provides a utility class StandardScaler that implements the Transformer API to compute the mean and standard deviation on a training set so as to be able to later reapply the same transformation on the testing set. This class is hence suitable for use in the early steps of a sklearn.pipeline.Pipeline

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