Factor analysis is a dimensionality reduction technique used to uncover latent variables (factors) that explain observed patterns of correlations in data. It is widely used in psychometrics, social sciences, and machine learning.
Exploratory Factor Analysis (EFA, Option A): Used when the underlying factor structure is unknown, aiming to discover potential latent variables.
Confirmatory Factor Analysis (CFA, Option B): Used when there is a hypothesis about factor structure, and the goal is to confirm it statistically.
Both are valid approaches to factor analysis, hence the correct answer is Option C (Both A and B).
[Reference:, DASCA Data Scientist Knowledge Framework (DSKF) – Dimensionality Reduction & Factor Analysis in Machine Learning., ]
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