In data analysis, what does normalization aim to achieve?

Normalization in data analysis aims to scale data into a common range, often to prepare it for algorithms that are sensitive to the scale of the data, such as distance-based methods like k-nearest neighbors or support vector machines. By transforming the features to the same scale, normalization ensures that no single feature dominates the others due to differences in magnitude. This is particularly important when features are measured in different units or have widely different ranges.

Typically, normalization involves techniques like min-max scaling, which transforms the data to a range between 0 and 1, or z-score normalization, which standardizes the data based on the mean and standard deviation. This process enhances the performance and training stability of various machine learning models.

Other options focus on different aspects of data handling. For example, reducing computational efficiency conflicts with the goal of normalization, which generally seeks to make models more efficient. Eliminating outliers, while a necessary process in some contexts, is not the primary goal of normalization; instead, it often aims at standardizing the scale of the data rather than removing specific values. Increasing dimensionality relates to processes like feature engineering or expansion, rather than scaling the existing features. Overall, the intent behind normalization is centered on achieving uniformity in the range of the