Why would you use repeated k-fold instead of standard k-fold?
mediumAnswer
- A single k-fold estimate has significant variance from the specific random split — especially on small data.
- Repeated k-fold runs k-fold R times with different seeds, averaging results.
- Cost: R * k fits.
- Benefit: much tighter estimate of generalization error (variance drops roughly by 1/R), and you get a distribution of scores for statistical tests.
- Common: 5-fold × 10 repeats.
- For very large data, one 5-fold pass is usually enough — variance is already low.
Check yourself — multiple choice
- It's slower and never useful
- Averages R runs of k-fold with different seeds to reduce estimate variance
- It only applies to time series
- It's the same as LOOCV
Repeated k-fold reduces split-variance in the estimated generalization error.
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