What is normalization?

Normalization is a method used in data processing and purification. Normalization’s major purpose is to make data homogeneous across all records and fields. It aids in the creation of a link between the entry data, which aids in the cleaning and improvement of data quality. Data standardization, on the other hand, is the act of putting disparate features on the same scale. In other words, standardized data may be described as rescaling the characteristics so that their mean is 0 and the standard deviation is 1.

Normalize or Standardize?

The debate about normalization vs. standardization is a persistent one among machine learning newbies. In this part, I’ll expand on the response.

  • When you know that your data does not follow a Gaussian distribution, normalization is a suitable option. This is important in algorithms like K-Nearest Neighbors and Neural Networks, which do not presume any data distribution.
  • In circumstances when the data follows a Gaussian distribution, standardization can be beneficial. This, however, does not have to be the case. Standardization, unlike normalization, does not have a boundary range. As a result, even if your data contains outliers, normalization will have no effect on them.

However, whether you use normalization or standardization depends on your problem and the machine learning algorithm you’re utilizing at the end of the day. When it comes to normalizing or standardizing your data, there is no hard and fast rule. To get the best results, start by fitting your model to raw, normalized, and standardized data and comparing the results.

Fitting the scaler to the training data and then using it to convert the testing data is a smart technique. During the model testing procedure, this would prevent any data from leaking. In most cases, scaling goal values isn’t necessary.

When to normalize and when to standardize data?

Data normalization is a sort of feature scaling that is only necessary when the data distribution is unknown or when the data does not have a Gaussian distribution. When the data has a diverse scope and the algorithms upon which data is being trained, such as Artificial Neural Network, do not make assumptions about the data distribution, this sort of scaling strategy is applied.

When using data for multivariate analysis, or when we want all the variables to be of comparable units, standardized data is frequently chosen. When the data has a bell curve or a gaussian distribution, it is generally used. No, this isn’t always true, but when applied to a Gaussian distribution, it’s thought to be more successful. When the data includes different ratios and the algorithms utilized make assumptions about the data distribution, this method comes in useful.

Key takeaways

  • When the data does not have a Gaussian distribution, normalization is employed, but standardization is used when the data does.
  • The normalization scales between [0,1] and [-1,1]. The range of standardization is unbounded.
  • Outliers have a significant impact on normalization. Outliers have a minor impact on standardization.
  • When the algorithms do not make assumptions about the data distribution, normalization is considered. When algorithms make assumptions about data distribution, standardization is applied.

Each of the aforementioned strategies plays a unique function in scaling data, and there are no hard and fast rules regarding which style of scaling to apply for certain data.