What is the primary goal of principal component analysis (PCA)?

The primary goal of principal component analysis (PCA) is to transform high-dimensional data into lower dimensions while preserving as much variance as possible. PCA achieves this by identifying the directions (or principal components) along which the data varies the most. By projecting the data onto these principal components, PCA reduces the dimensionality, making it easier to visualize, interpret, and analyze the data without significant loss of important information.

This technique is particularly valuable in situations where high-dimensional data can lead to computational inefficiencies or challenges in data interpretation. By retaining the most critical features of the data through variance preservation, PCA allows for more effective data modeling and analysis.

In contrast, options that suggest creating generative models, increasing dimensionality, or identifying and eliminating data bias do not align with the primary purpose of PCA. Generative models focus on learning the underlying distribution of data, increasing dimensionality contradicts the goal of PCA to simplify data, and addressing data bias pertains to data preprocessing rather than dimensionality reduction methods like PCA.