#out-of-bag_error
Out-of-bag error
Method of measuring prediction error
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). Bagging uses subsampling with replacement to create training samples for the model to learn from. OOB error is the mean prediction error on each training sample xi, using only the trees that did not have xi in their bootstrap sample.
Mon 29th
Provided by Wikipedia
This keyword could refer to multiple things. Here are some suggestions:
0 searches
This keyword has never been searched before
This keyword has never been searched for with any other keyword.