Main Content

**Class: **TreeBagger

Out-of-bag quantile loss of bag of regression trees

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.

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.

[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.