Normalization和Standardization的差异
Normalization:
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使数值在0-1之间
Standardization （又称为Z-score normalization）:
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使均值为0，且标准差为1
Normalization is good to use when you know that the distribution of your data does not follow a Gaussian distribution. This can be useful in algorithms that do not assume any distribution of the data like K-Nearest Neighbors and Neural Networks.
Standardization, on the other hand, can be helpful in cases where the data follows a Gaussian distribution. However, this does not have to be necessarily true. Also, unlike normalization, standardization does not have a bounding range. So, even if you have outliers in your data, they will not be affected by standardization.
两种都可以被称为scaling。数据符合高斯分布的时候可以用standardization，不符合高斯分布的时候可以用normalization。Normalization适合于不考虑数据分布的模型，例如KNN和神经网络。
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