Normalization and normalization _ normalization of

Some classifiers need to calculate the distance (such as Euclidean distance) between samples, such as KNN. If a feature range is very large, then the distance calculation depends largely on this feature, which is inconsistent with the actual situation (for example, when the actual situation is a small range of features more important). 3 normalized type 1) linear normalization

This normalization method is more applicable in the case of numerical comparison. This method has a flaw, if Max and Min is unstable, it is easy to make the normalized results unstable, so that the subsequent use of the effect is not stable. You can use empirical constant values instead of Max and min in practice.

2) Standard deviation standardization

The processed data conforms to the standard normal distribution, that is, the mean value is 0, the standard deviation is 1, and the conversion function is:

Where μ is the mean value of all sample data, Σ is the standard deviation of all sample data.