`err = oobError(B)`

err = oobError(B,'param1',val1,'param2',val2,...)

`err = oobError(B)`

computes the misclassification
probability (for classification trees) or mean squared error (for
regression trees) for out-of-bag observations in the training data,
using the trained bagger `B`

. `err`

is
a vector of length `NTrees`

, where `NTrees`

is
the number of trees in the ensemble.

`err = oobError(B,'param1',val1,'param2',val2,...) `

specifies
optional parameter name/value pairs:

`'Mode'` | Character vector indicating how `oobError` computes
errors. If set to `'cumulative'` (default), the method
computes cumulative errors and `err` is a vector
of length `NTrees` , where the first element gives
error from `trees(1)` , second element gives error
from `trees(1:2)` etc., up to `trees(1:NTrees)` .
If set to `'individual'` , `err` is
a vector of length `NTrees` , where each element is
an error from each tree in the ensemble. If set to `'ensemble'` , `err` is
a scalar showing the cumulative error for the entire ensemble. |

`'Trees'` | Vector of indices indicating what trees to include in this
calculation. By default, this argument is set to `'all'` and
the method uses all trees. If `'Trees'` is a numeric
vector, the method returns a vector of length `NTrees` for `'cumulative'` and `'individual'` modes,
where `NTrees` is the number of elements in the input
vector, and a scalar for `'ensemble'` mode. For example,
in the `'cumulative'` mode, the first element gives
error from `trees(1)` , the second element gives error
from `trees(1:2)` etc. |

`'TreeWeights'` | Vector of tree weights. This vector must have the same length
as the `'Trees'` vector. `oobError` uses
these weights to combine output from the specified trees by taking
a weighted average instead of the simple nonweighted majority vote.
You cannot use this argument in the `'individual'` mode. |

`oobError`

estimates the weighted ensemble
error for out-of-bag observations. That is, `oobError`

applies `error`

to
the training data stored in the input `TreeBagger`

model `B`

,
and selects the out-of-bag observations for each tree to compose the
ensemble error.

`B.X`

and`B.Y`

are the training data predictors and responses, respectively.`B.OOBIndices`

specifies which observations are out-of-bag for each tree in the ensemble.`B.W`

specifies the observation weights.Optionally:

Using the

`'Mode'`

name-value pair argument, you can specify to return the individual, weighted ensemble error for each tree, or the entire, weighted ensemble error. By default,`oobError`

returns the cumulative, weighted ensemble error.Using the

`'Trees'`

name-value pair argument, you can choose which trees to use in the ensemble error calculations.Using the

`'TreeWeights'`

name-value pair argument, you can attribute each tree with a weight.

`oobError`

applies the algorithms described
below. For more details, see `error`

and `predict`

.

For regression problems, `oobError`

returns
the weighted MSE.

`oobError`

predicts responses for all out-of-bag observations.The MSE estimate depends on the value of

`'Mode'`

.If you specify

`'Mode','Individual'`

, then`oobError`

sets any in bag observations within a selected tree to the weighted sample average of the observed, training data responses. Then,`oobError`

computes the weighted MSE for each selected tree.If you specify

`'Mode','Cumulative'`

, then`ooError`

returns a vector of cumulative, weighted MSEs, where MSE_{t}is the cumulative, weighted MSE for selected tree*t*. To compute MSE_{t}, for each observation that is out of bag for at least one tree through tree*t*,`oobError`

computes the cumulative, weighted mean of the predicted responses through tree*t*.`oobError`

sets observations that are in bag for all selected trees through tree*t*to the weighted sample average of the observed, training data responses. Then,`oobError`

computes MSE_{t}.If you specify

`'Mode','Ensemble'`

, then, for each observation that is out of bag for at least one tree,`oobError`

computes the weighted mean over all selected trees.`oobError`

sets observations that are in bag for all selected trees to the weighted sample average of the observed, training data responses. Then,`oobError`

computes the weighted MSE, which is the same as the final, cumulative, weighted MSE.

In classification problems, `oobError`

returns
the weighted misclassification rate.

`oobError`

predicts classes for all out-of-bag observations.The weighted misclassification rate estimate depends on the value of

`'Mode'`

.If you specify

`'Mode','Individual'`

, then`oobError`

sets any in bag observations within a selected tree to the predicted, weighted, most popular class over all training responses. If there are multiple most popular classes,`error`

considers the one listed first in the`ClassNames`

property of the`TreeBagger`

model the most popular. Then,`oobError`

computes the weighted misclassification rate for each selected tree.If you specify

`'Mode','Cumulative'`

, then`ooError`

returns a vector of cumulative, weighted misclassification rates, where*e*_{t}^{*}is the cumulative, weighted misclassification rate for selected tree*t*. To compute*e*_{t}^{*}, for each observation that is out of bag for at least one tree through tree*t*,`oobError`

finds the predicted, cumulative, weighted most popular class through tree*t*.`oobError`

sets observations that are in bag for all selected trees through tree*t*to the weighted, most popular class over all training responses. If there are multiple most popular classes,`error`

considers the one listed first in the`ClassNames`

property of the`TreeBagger`

model the most popular. Then,`oobError`

computes*e*_{t}^{*}.If you specify

`'Mode','Ensemble'`

, then, for each observation that is out of bag for at least one tree,`oobError`

computes the weighted, most popular class over all selected trees.`oobError`

sets observations that are in bag for all selected trees through tree*t*to the predicted, weighted, most popular class over all training responses. If there are multiple most popular classes,`error`

considers the one listed first in the`ClassNames`

property of the`TreeBagger`

model the most popular. Then,`oobError`

computes the weighted misclassification rate , which is the same as the final, cumulative, weighted misclassification rate.

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