Out-of-bag error
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Out-of-bag (OOB) error, also called out-of-bag estimate, is a method of measuring the prediction error of random forests, boosted decision trees, and other machine learning models utilizing bootstrap aggregating to sub-sample data sampled used for training. OOB is the mean prediction error on each training sample xᵢ, using only the trees that did not have xᵢ in their bootstrap sample.[1]
Subsampling allows one to define an out-of-bag estimate of the prediction performance improvement by evaluating predictions on those observations which were not used in the building of the next base learner. Out-of-bag estimates help avoid the need for an independent validation dataset, but often underestimate actual performance improvement and the optimal number of iterations.[2]
See also
- Boosting (meta-algorithm)
- Bootstrapping (statistics)
- Cross-validation (statistics)
- Random forest
- Random subspace method (attribute bagging)
References
- ↑ Lua error in package.lua at line 80: module 'strict' not found.
- ↑ Ridgeway, Greg (2007). Generalized Boosted Models: A guide to the gbm package.
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