What is the standardization of input variables' mean referred to in neural network modeling?

In the context of neural network modeling, the standardization of input variables' mean refers to the process of adjusting the input data so that it has a mean of zero and a standard deviation of one. This technique is particularly important because neural networks are sensitive to the scale of the input data. When features are on different scales, it can lead to slower convergence during training or may even cause the model to perform poorly.

Standardization involves subtracting the mean of each input variable from the data and then dividing by the standard deviation. This results in each input variable having the same mean and variance, which helps the learning algorithm perform more efficiently. By using standardization, the neural network can better learn the patterns in the data without being impeded by the influence of varying scales of different features.

Other options refer to different concepts within data preprocessing or statistical analysis. Normalization, for instance, typically refers to scaling data to a certain range, such as [0, 1], rather than adjusting the mean and standard deviation. Standard deviation and variance adjustment relate to measuring variability in data or making changes to the variance of data distribution, but they do not specifically describe the process of centering the data around a mean of zero and scaling by standard deviation, which is