So one day I took a course on in-depth learning and there I was asked “Which data is better normalized or standardized?” I thought about it, and what I read or found, I just wanted to share it here with the community. So before we start the comparison and all we need to first understand the basic definition of Normalized Data and Standardized Data.
Normalization is part of data processing and cleansing techniques. The main goal of normalization is to make the data homogeneous for all records and fields. It helps to establish a connection between the input data, which in turn helps to clean up and improve the quality of the data. Data standardization is the process of putting different features on the same scale. In other words, standardized data can be defined as scalable for attributes so that their mean is 0 and the standard deviation becomes 1.
Data normalization is a form of property scaling and is only required when data sharing is not known or if the data does not have a Gaussian distribution. This type of scaling method is used when the data has a diverse scope and the algorithms into which the data is trained do not make assumptions about the distribution of the data, such as an artificial neural network.
Standardized data are generally preferred when the data are used for multivariate analyzes, i.e., when we want all variables of comparable units. It is usually used when the data has a clock curve, i.e. it has a Gaussian distribution. No, this is not always true, but it is considered more effective when used in Gaussian distribution. This technique is convenient when the data has a variable relationship and the algorithms used, making assumptions about the data distribution, such as logistic regression, linear discriminant analysis, etc.
- Normalization is used when the data does not have a Gaussian distribution, while Standardization is used when the data has a Gaussian distribution.
- Normalization on a scale [0,1] or [-1,1]. Standardization is not limited by the area.
- Deviations strongly affect normalization. Deviations have a small effect on standardization.
- Normalization is considered when algorithms do not make assumptions about data distribution. Standardization is used when algorithms make assumptions about data sharing.
Each of the above techniques has its own role in scaling data, and unambiguous rules do not specify what type of scaling is used for a particular piece of data. Personally, I try to monitor the distribution of data. Except that I try to apply different algorithms to raw, normalized, standardized data and compare and evaluate their results and choose the technique with the best result.
Note: “Keep it simple, when you go too complicated, you forget the obvious.“