## Documentation Center |

`mar = margin(B,X,Y)mar = margin(B,X,Y,'param1',val1,'param2',val2,...)`

`mar = margin(B,X,Y)` computes the classification
margins for predictors `X` given true response `Y`.
The `Y` can be either a numeric vector, character
matrix, cell array of strings, categorical vector or logical vector.
`mar` is a numeric array of size `Nobs`-by-`NTrees`,
where `Nobs` is the number of rows of `X` and `Y`,
and `NTrees` is the number of trees in the ensemble `B`.
For observation `I` and tree `J`, `mar(I,J)` is
the difference between the score for the true class and the largest
score for other classes. This method is available for classification
ensembles only.

`mar = margin(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), margin computes
cumulative errors and mar is an Nobs-by-NTrees matrix,
where the first column gives error from trees(1),
second column gives error fromtrees(1:2) etc,
up to trees(1:NTrees). If set to 'individual', mar is
a Nobs-by-NTrees matrix, where
each element is an error from each tree in the ensemble. If set to 'ensemble', mar a
single column of length Nobs showing the cumulative
margins 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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