What is the purpose of normalization during data preprocessing?

Normalization during data preprocessing is primarily aimed at scaling features to a common range while maintaining the distinctions between them. This is important because many machine learning algorithms are sensitive to the scale of the data. For instance, algorithms that use distance calculations, such as k-nearest neighbors or support vector machines, can be significantly affected if the features are not on a similar scale. Normalization helps to bring different features into a standardized format, typically scaling them to a range between 0 and 1 or -1 and 1. By doing so, it ensures that each feature contributes equally to the distance computation and ultimately to the model training process.

Additionally, normalization can help speed up convergence in algorithms that use gradient descent, improving overall training efficiency. Preserving the differences between features while achieving a common scale is crucial for the effective performance of many predictive models.