python - Scikit-learn:preprocessing.scale() 与 preprocessing.StandardScaler()

Question

我理解缩放意味着以均值 (mean=0) 为中心并使单位方差 (variance=1)。

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

Answer 1

它们做的完全一样，但是:

• 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

我会一直使用后者，即使我不需要 inverse_transform 和 co。由 StandardScaler() 支持。

摘自 docs :

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

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