oobQuantileError
Out-of-bag quantile loss of bag of regression trees
Description
returns half of the out-of-bag mean absolute deviation
(MAD) from comparing the true responses in err
= oobQuantileError(Mdl
)Mdl.Y
to the predicted,
out-of-bag medians at Mdl.X
, the predictor data, and using the bag of
regression trees Mdl
. Mdl
must be a TreeBagger
model object.
uses additional options specified by one or more err
= oobQuantileError(Mdl
,Name,Value
)Name,Value
pair
arguments. For example, specify quantile probabilities, the error type, or which trees to
include in the quantile-regression-error estimation.
Input Arguments
Name-Value Arguments
Output Arguments
Examples
More About
Tips
The out-of-bag ensemble error estimator is unbiased for the true ensemble error. So, to tune parameters of a random forest, estimate the out-of-bag ensemble error instead of implementing cross-validation.
References
[1] Breiman, L. "Random Forests." Machine Learning 45, pp. 5–32, 2001.
[2] Meinshausen, N. “Quantile Regression Forests.” Journal of Machine Learning Research, Vol. 7, 2006, pp. 983–999.
Version History
Introduced in R2016b