How would you approach reducing the dimensionality of a dataset?

Example Answer

I would start by understanding why I want dimensionality reduction in the first place. Sometimes the goal is faster training, sometimes it is reducing noise, sometimes it is visualization, and sometimes it is making the model less prone to overfitting. The right method depends on that goal.

If I want a simple linear compression, I might start with PCA after checking feature scaling. If the structure is more complex, I might explore autoencoders or task-specific feature selection. I would also validate whether the reduced representation preserves the signal that actually matters for the downstream task. Dimensionality reduction is only helpful if it removes redundancy and noise without discarding information the model needs to perform well.

Common Poor Answer

A weak answer says use PCA because it reduces dimensions, without explaining what problem it is solving or how to verify that important signal was not lost.