How does the curse of dimensionality affect model generalization?
Updated May 15, 2026
Short answer
It reduces generalization by increasing variance and sparsity.
Deep explanation
High-dimensional spaces require exponentially more data to generalize well.
Real-world example
Fraud detection models overfitting rare patterns.
Common mistakes
- Ignoring dataset size requirements.
Follow-up questions
- What is VC dimension?
- How to improve generalization?