→ Principal Component Analysis (PCA) is an unsupervised technique used to reduce the dimensionality of large datasets by transforming correlated features into a smaller set of uncorrelated components (principal components) while retaining the most variance.
Why the other options are incorrect:
B: Classification is a predictive modeling task; PCA is not inherently predictive.
C: Regression models numerical relationships; PCA does not predict outcomes.
D: Recommendation systems use collaborative or content filtering, not PCA directly.
Official References:
CompTIA DataX (DY0-001) Study Guide – Section 3.3:“PCA is primarily used for reducing the number of variables while preserving data structure and minimizing information loss.â€
Pattern Recognition and Machine Learning, Chapter 12:“PCA identifies principal axes of variation and is widely used in preprocessing for dimensionality reduction.â€
