Normalization refers to a process where the values within a dataset are scaled and transformed in a way that they share a common scale, while maintaining the relative rank and order of the original values intact. It is a prevalent preprocessing step in machine learning (ML). This step is crucial as it ensures that the data is in a uniform format and is well-suited for application with ML algorithms.
There are multiple methods to perform normalization, such as standardization, z-score normalization, and min-max normalization. Min-max normalization scales the data in a manner that all values are mapped to a predefined range, usually between 0 and 1. Standardization transforms the data to have a mean of 0 and a standard deviation of 1. Z-score normalization, by applying the z-score formula, also scales the data to achieve a mean of 0 and a standard deviation of 1.
Normalization is frequently employed to guarantee that the data meets the requirements of ML algorithms. Many of these algorithms presume that the data follows a normal distribution and that the features have comparable scales. Additionally, normalization can mitigate the influence of outliers and enhance the performance of certain algorithms.