What is the purpose of normalization when preparing datasets?

Normalization serves the specific purpose of scaling data to a particular range, typically between 0 and 1 or -1 and 1. This process is crucial when working with data that has varying scales or units, as it helps to ensure that all features contribute equally to the distance calculations in many machine learning algorithms. For instance, when attributes like age and income are involved in a model, their ranges might differ significantly. Normalization mitigates the risk that larger-valued attributes could disproportionately influence the outcome of the analysis.

By scaling the data, normalization not only enhances the performance of certain algorithms, like gradient descent or k-means clustering, but also promotes convergence and accelerates training times. This practice is particularly vital in neural networks where varying scales can impede learning efficiency. Thus, the correct choice reflects the main objective of normalization in the context of dataset preparation.