Normalization vs Standardization in Data Preprocessing for Machine Learning Models

Normalization and Standardization are two common techniques used in data preprocessing for machine learning models. Understanding the difference between them is crucial in ensuring the optimal performance of your models.

Normalization:

Normalization is the process of rescaling the features of a dataset to have a common scale without distorting differences in the range of values. It typically involves scaling the data to a range of [0, 1] or [-1, 1]. Normalization helps in avoiding biases towards features with larger scales and can improve the convergence of machine learning algorithms.

Standardization:

Standardization, on the other hand, involves transforming the data to have a mean of 0 and a standard deviation of 1. It centers the data around 0 and scales it by the standard deviation, making it more suitable for algorithms that assume normally distributed data. Standardization is also known as z-score normalization.

In summary, normalization adjusts the scale of features to a fixed range, while standardization centers the data around 0 and scales it by the standard deviation. The choice between normalization and standardization depends on the characteristics of the dataset and the requirements of the machine learning algorithm being used.