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 (bagging) to sub-sample data samples 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 underestimates actual performance improvement and the optimal number of iterations.[2]

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References[edit]

  1. ^ Gareth James; Daniela Witten; Trevor Hastie; Robert Tibshirani (2013). An Introduction to Statistical Learning. Springer. pp. 316–321. 
  2. ^ Ridgeway, Greg (2007). Generalized Boosted Models: A guide to the gbm package.