What is random subspace method in ensemble learning?
Updated May 16, 2026
Short answer
Random subspace method trains models on randomly selected feature subsets.
Deep explanation
The random subspace method builds each base learner using only a subset of features instead of all features. This increases diversity among models and reduces overfitting, especially in high-dimensional datasets. It is commonly used in Random Forest and ensemble feature selection systems.
Real-world example
Used in genomic datasets where features are extremely high-dimensional.
Common mistakes
- Using too few features per model leading to underfitting.
Follow-up questions
- How does it improve diversity?
- Is it used in Random Forest?