**Class: **TreeBagger

Quantile predictions for out-of-bag observations from bag of regression trees

`YFit = oobQuantilePredict(Mdl)`

`YFit = oobQuantilePredict(Mdl,Name,Value)`

```
[YFit,YW]
= oobQuantilePredict(___)
```

returns
a vector of medians of the predicted responses at all out-of-bag observations
in `YFit`

= oobQuantilePredict(`Mdl`

)`Mdl.X`

, the predictor data, and using `Mdl`

,
which is a bag of regression trees. `Mdl`

must be
a `TreeBagger`

model
object and `Mdl.OOBIndices`

must be nonempty.

uses
additional options specified by one or more `YFit`

= oobQuantilePredict(`Mdl`

,`Name,Value`

)`Name,Value`

pair
arguments. For example, specify quantile probabilities or trees to
include for quantile estimation.

`[`

also returns a sparse
matrix of response
weights using any of the previous syntaxes.`YFit`

,`YW`

]
= oobQuantilePredict(___)

`oobQuantilePredict`

estimates out-of-bag quantiles
by applying `quantilePredict`

to all observations in the
training data (`Mdl.X`

). For each observation, the
method uses only the trees for which the observation is out-of-bag.

For observations that are in-bag for all trees in the ensemble, `oobQuantilePredict`

assigns
the sample quantile of the response data. In other words, `oobQuantilePredict`

does
not use quantile regression for out-of-bag observations. Instead,
it assigns `quantile(Mdl.Y,`

,
where * tau*)

`tau`

`Quantile`

name-value
pair argument.[1] Meinshausen, N. “Quantile Regression
Forests.” *Journal of Machine Learning Research*,
Vol. 7, 2006, pp. 983–999.

[2] Breiman, L. “Random Forests.” *Machine
Learning*. Vol. 45, 2001, pp. 5–32.