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Question
What is dimensionality reduction?
Answer
Describing the same data with **fewer features** while keeping as much of the variation as possible.
Question
What is the curse of dimensionality?
Answer
More features means an **exponentially larger space**, so data becomes sparse, distances lose meaning, and exponentially more records are needed.
Question
Feature selection or extraction — what is the difference?
Answer
**Selection** keeps a subset of the original columns. **Extraction** builds new features from combinations of them.
Question
Name four problems the curse of dimensionality causes.
Answer
**Overfitting**, higher **computational cost** and **memory use**, and distance metrics that **stop meaning anything**. The data also becomes hard to visualise.
Question
What is always lost in dimensionality reduction?
Answer
Some **variation** — it is lossy by construction — and with extraction, **interpretability**: a component cannot be named in plain words.
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Full study notes for Dimensionality reduction
Topic 4.2 hub
Data preprocessing
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