What is the trade-off between interpretability and performance in dimensionality reduction?

More powerful methods often reduce interpretability while improving performance.

Feature selection methods preserve original feature meaning but may miss complex interactions. Feature extraction methods like PCA or autoencoders improve representation power but produce latent variables that are harder to interpret. This trade-off is critical in regulated domains like healthcare or finance.