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(文档).
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 (documentation).
有人可以提供更详细的解释吗?
Can someone give a more extended explanation?
推荐答案
我在此线程中提供了更多详细信息，但我也要在这里解释一下.
I have provided more details in this thread, but let me just explain this here as well.
数据标准化(每个列/功能/每个变量)涉及以下方程式:
The standardation of the data (each column/feature/variable indivivually) involves the following equations:
说明:
如果将with_mean和with_std设置为False，则将平均值μ设置为0，将std设置为1，假定列/特征来自正态高斯.分布(平均值为0，标准差为1).
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).
如果将with_mean和with_std设置为True，则实际上将使用数据的真实μ和σ.这是最常见的方法.
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.
这篇关于"with_std = False或True"之间的StandardScaler差异和"with_mean = False或True"；的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！