Practice Flashcards
Flip to reveal answersWhat is dimensionality reduction?
Track your progress — Sign up free to save your progress and get smart review reminders based on spaced repetition.
All 5 Flashcards — Dimensionality reduction
Sign up free to track progress and get spaced-repetition review schedules.
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, as PCA does.
Question
What does PCA do?
Answer
Finds the directions along which the data varies **most** and uses them as the new axes, so the first few components carry most of the information.
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.
Read the notes
Full study notes for Dimensionality reduction
Topic 4.2 hub
Data preprocessing
More from Topic 4.2
All flashcards in this topic
Computer Science exam skills
Paper structures & tips
Track your progress with spaced repetition
Sign up free — Aimnova tells you exactly which cards to review and when, so you remember everything before your IB exam.
Start Free