What is stability selection?
hardAnswer
- Run your feature-selection method (usually Lasso) on many random subsamples of the data.
- Record how often each feature is selected.
- Keep only features selected in ≥ (typically 60-90%) of subsamples.
- Rationale: any single Lasso fit is unstable — features come and go with the sample.
- Stability selection isolates features that are robustly informative across data resamples, with theoretical false-discovery-rate guarantees.
Check yourself — multiple choice
- Select features by variance
- Repeated Lasso on subsamples; keep features selected often — theoretically bounded FDR
- Only for classification
- It's the same as bagging
Stability selection: repeated Lasso + keep features selected in ≥ threshold fraction of runs.
#feature-selection#regularization
Practise Supervised Learning
215 interview questions in this topic.
Related questions
- Why does L1 (Lasso) produce sparse coefficients but L2 (Ridge) does not?
- When would you use Group Lasso instead of standard Lasso?
- How do you detect and fix overfitting?
- L1 vs L2 regularization — what's the difference?
- What is the generalization gap and how do you shrink it?
- How do you handle strong multicollinearity in a linear model?