Note: This page has been translated by MathWorks. Please click here

To view all translated materals including this page, select Japan from the country navigator on the bottom of this page.

To view all translated materals including this page, select Japan from the country navigator on the bottom of this page.

`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.

Was this topic helpful?