`err = error(B,X,Y) `

err = error(B,X,Y,'param1',val1,'param2',val2,...)

`err = error(B,X,Y) `

computes the misclassification
probability (for classification trees) or mean squared error (MSE,
for regression trees) for each tree, for predictors `X`

given
true response `Y`

. For classification, `Y`

can
be either a numeric vector, character matrix, cell array of strings,
categorical vector or logical vector. For regression, `Y`

must
be a numeric vector. `err`

is a vector with one
error measure for each of the `NTrees`

trees in the
ensemble `B`

.

`err = error(B,X,Y,'param1',val1,'param2',val2,...)`

specifies
optional parameter name/value pairs:

`'mode'` | String indicating how the method computes errors. If set to `'cumulative'` (default), `error` 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. The method uses these weights
to combine output from the specified trees by taking a weighted average
instead of the simple non-weighted majority vote. You cannot use this
argument in the `'individual'` mode. |

`'useifort'` | Logical matrix of size `Nobs` -by-`NTrees` indicating
which trees should be used to make predictions for each observation.
By default the method uses all trees for all observations. |

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