“Out-of-bag error”的意思、由来-开放百科全书

词条

Out-of-bag error

释义

See also
References

{{Machine learning bar}}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 {{mvar|xᵢ}}, using only the trees that did not have {{mvar|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]

References

1. ^{{cite book |first1=Gareth |last1=James |first2=Daniela |last2=Witten |first3=Trevor |last3=Hastie |first4=Robert |last4=Tibshirani |title=An Introduction to Statistical Learning |publisher=Springer |year=2013 |url=http://www-bcf.usc.edu/~gareth/ISL/ |pages=316–321}}
2. ^{{cite web |last=Ridgeway |first=Greg |authorlink=Greg Ridgeway |year=2017 |url=https://cran.r-project.org/web/packages/gbm/gbm.pdf |title=Generalized Boosted Models: A guide to the gbm package }}

3 : Ensemble learning|Machine learning algorithms|Computational statistics

随便看

开放百科全书收录14589846条英语、德语、日语等多语种百科知识，基本涵盖了大多数领域的百科知识，是一部内容自由、开放的电子版国际百科全书。

See also

References