What is the main purpose of scaling features in machine learning?

Scaling features in machine learning is primarily aimed at standardizing the range of independent variables. This is crucial because many machine learning algorithms, particularly those based on optimization techniques like gradient descent, are sensitive to the scale of the input data. If the features have vastly different ranges, the algorithm may converge slowly or even struggle to find the optimal solution, as it may take larger steps on some features and smaller steps on others, leading to inefficiencies.

For example, when using algorithms like k-means clustering, support vector machines, or neural networks, scaling ensures that the contributions of all features are weighted equally in the calculations. This balancing act helps the model to learn more effectively since it can focus on finding patterns rather than being influenced by the differing magnitudes of the features.

In contrast to this, the creation of new features through combinations is an approach to feature engineering. Eliminating outliers is a method related to data cleaning, and while reducing computation time can be a secondary benefit of scaling, it is not the primary purpose. Thus, standardizing the range of features stands out as the essential role of scaling in the context of machine learning.