Which method specifically removes attributes from a dataset?

Dimensionality reduction is the correct choice as it specifically focuses on reducing the number of features or attributes in a dataset while preserving as much relevant information as possible. This method is particularly useful when dealing with high-dimensional data, where having too many attributes can lead to problems like overfitting and increased computational costs. Techniques such as Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) are commonly used for dimensionality reduction, allowing for a more manageable dataset that still retains the essential characteristics needed for analysis.

In contrast, data auditing refers to the process of assessing the quality and reliability of data. Numerosity reduction involves reducing the size of data representations while keeping all attributes intact, rather than removing them. Data profiling analyzes the data to understand its structure, content, and relationships but does not directly remove attributes from the dataset. Understanding these distinctions clarifies why dimensionality reduction is specifically aimed at removing attributes effectively.