What is Boruta and when do you reach for it?
hardAnswer
- A wrapper feature selection algorithm that compares each feature's importance to that of 'shadow' features — random permutations of that feature.
- A feature is confirmed if its importance is significantly higher than the max shadow importance across multiple random forest fits (statistical test with Bonferroni correction).
- Slow (many RF fits) but very rigorous — often used in biology / genomics where reliability matters more than speed.
- Use when you need statistical confidence that selected features are truly informative.
Check yourself — multiple choice
- Boruta is a scikit-learn classifier
- A RF wrapper: keep features whose importance beats their permuted 'shadows' with statistical significance
- Same as Lasso
- It's only for time series
Boruta: features must beat random-shadow importance across RF fits with statistical significance.
#feature-selection#random-forest
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