The sklearn documentation for gridsearch (link) puts " Out of Bag Estimates" in a subsection under "3.2.4. Alternatives to brute force parameter search." I understand each of grid search and OOB, but I don't understand how it's an "alternative." For example, if I need to determine the ideal max_features parameter to use with RandomForestClassifier, how would I use OOB instead of GridSearch? I can imagine for example using GridSearch with the scoring parameter being a callable returning the OOB, but it's not really an alternative as much as a "complementary" feature.


Both OOB and CV try to provide honest estimates of performance. OOB is basically "for free", while CV is more accurate.

So instead of picking the optimal column subsampling proportion by CV, you could try different values and pick the one with best OOB score.

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    $\begingroup$ How would you use OOB (without grid search) to find if max features=5 is better than 10? If it's an "alternative", there must be a way to use OOB without grid search $\endgroup$ – shadi Jan 14 '18 at 17:22
  • $\begingroup$ There's not, its poor wording. $\endgroup$ – Matthew Drury Jan 14 '18 at 20:48

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