sklearn StandardScaler与“ with_std = False或True”之间的差异和“ with_mean = False或True”； [英] sklearn StandardScaler differece between "with_std=False or True" and "with_mean=False or True"

问题描述

我正在尝试标准化一些数据，以便能够对其应用PCA。我正在使用sklearn.preprocessing.StandardScaler。我很难理解在参数 with_mean和 with_std中使用 True或 False之间的区别。以下是命令的说明：

---

说明：

如果将 with_mean 和 with_std 设置为 False ，则将平均值μ设置为 0 并 std 设为1，假定列/特征来自正态高斯分布（均值为0和1 std）。

如果将 with_mean 和 with_std 设置为 True ，那么您实际上将使用数据的真实μ和σ。这是最常见的方法。

I am trying to standardize some data to be able to apply PCA to it. I am using sklearn.preprocessing.StandardScaler. I am having trouble to understand the difference between using "True" or "False" in the parameters "with_mean" and "with_std". Here is the description of the command:

https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html

Can someone give a more extended explanation?

Thank you very much!

解决方案

I have provided more details in this post https://stackoverflow.com/a/50879522/5025009, but let me just explain this here as well.

The standardation of the data (each column/feature/variable indivivually) involves the following equations:

---

Explanation:

If you set with_mean and with_std to False, then the mean μ is set to 0 and the std to 1, assuming that the columns/features are coming from the normal gaussian distribution (which has 0 mean and 1 std).

If you set with_mean and with_std to True, then you will actually use the true μ and σ of your data. This is the most common approach.

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