Principal Component Analysis for Dimensionality Reduction
PCA projects high-dimensional data onto the directions of greatest variance. The practical question is how many components to keep: enough to retain information, few enough to keep models simple and avoid overfitting.
Focus areas
- Applying Principal Component Analysis (PCA) to reduce the dimensionality of high-dimensional feature sets while preserving maximum variance for downstream model training.
- Evaluating explained-variance thresholds and eigenvalue decomposition to balance information retention against model complexity and overfitting risk.
Topics
PCAFeature EngineeringHigh-Dimensional Data