What is the function of “feature scaling” in machine learning models?

Feature scaling is crucial in machine learning because it standardizes or normalizes the range of features in the dataset. Many machine learning algorithms, particularly those that rely on distance metrics (like k-nearest neighbors or support vector machines), are sensitive to the scale of the input data. If the features have different scales, the model may perform poorly, as features with larger ranges can dominate the learning process, leading to biased predictions.

By applying feature scaling, either through normalization (transforming features to a range between 0 and 1) or standardization (transforming features to have a mean of 0 and a standard deviation of 1), all features contribute equally to the model training. This helps improve convergence speed in gradient descent and often leads to better model performance.

Other choices focus on aspects that do not directly relate to the core purpose of feature scaling. Increasing the variety of data inputs does not address the challenge posed by differing feature scales. Selecting only the most important features is about feature selection rather than scaling, and while feature scaling can contribute to simpler models, its primary function is not to reduce overall model complexity but rather to ensure that all features are treated equally in terms of their mathematical influence during the training process.