What is the primary purpose of data normalization?

The primary purpose of data normalization is to scale data within a specified range, typically between 0 and 1 or -1 and 1. This process is essential in preparing data for machine learning algorithms and statistical models, as it ensures that each feature contributes equally to the distance calculations or optimization processes. When features are on different scales, algorithms like gradient descent may struggle to converge efficiently, leading to suboptimal performance. By normalizing the data, we mitigate the impact of varying scales and help improve convergence and overall model performance.

While data normalization may sometimes indirectly influence other aspects like reducing redundancy or enhancing visualization, its core function is focused on scaling data. Balancing a dataset is more closely related to techniques like oversampling or undersampling, which aim to address class imbalances rather than standardizing feature scales.