# Does REPEATED K-fold cross validation make sense with Random Forest? [closed]

When using random forest, would using normal cross-validation and just taking the average results from multiple models with different random states give me the same results as using Repeated K-fold cross validation?

Repeated K-fold cross-validation basically repeats cross-validation with multiple different splits of the data and reports the average results.

• Why you think that the helpfulness or not of a general technique, like repeated k-fold CV, depends on the specific ML algorithm used (RF or otherwise)? – desertnaut Mar 23 at 14:21
• I edited the question. Basically I want to know if not doing repeated K-fold CV would still give me the same results if I just average a lot of RF models with different random states. – Artur D Mar 23 at 18:26

The reason is that fixing one k-fold split and then repeatedly fitting random forests (with different random seeds) still only gives each forest access to $$(k-1)/k$$ of the data at a time. It may be easiest to think about the case when the number of trees is astronomical: the random choices for the bagging get averaged out, to the point where different random seeds don't actually matter: the forests converge to the same result, given a training split. Then the average of the forests' scores are the same for each of the splits, and so you average just $$k$$ scores. Compare that to repeated $$k$$-fold, where each of the forests converges, but are all on different training sets, and so the average happens with more variety.
Whether that has a sizable impact, or in which direction, is harder to say. Repeated $$k$$-fold seems like it should give more stable results, even when the number of trees is something more reasonable, because the forests are less correlated.