Data normalization methods and their advantages

(1) Normalization

  Converting the data to decimals in the (0,1) interval makes data processing convenient, and mapping the data to the range of 0 to 1 for processing is more convenient and faster.

(2) The advantages of normalization

1. After normalization, the speed of gradient descent to find the optimal solution is accelerated. If the machine learning model uses the gradient descent method to find the optimal solution, normalization is often very necessary, otherwise it will be difficult or even impossible to converge.
2. .Normalization has the potential to improve accuracy.

(3) Normalization method

1. min-max标准化
   $$x_{normalization}=\frac{x-Min}{Max-Min}$$

2. Z-score标准化
   $$x_{normalization}=\frac{x-}{p}$$

• $\mu$ : the mean of all the original data ($mean$)
• $\sigma$ : standard deviation of all original data ($standarddeviation$)
• The processed data conform to a standard normal distribution with a mean of 0 and a standard deviation of 1