`mar = margin(B,TBLnew,Ynew)`

mar = margin(B,Xnew,Ynew)

mar = margin(B,TBLnew,Ynew,'param1',val1,'param2',val2,...)

mar
= margin(B,Xnew,Ynew,'param1',val1,'param2',val2,...)

`mar = margin(B,TBLnew,Ynew)`

computes the
classification margins for the predictors contained in the table `TBLnew`

given
true response `Ynew`

. You can omit `Ynew`

if `TBLnew`

contains
the response variable. If you trained `B`

using sample
data contained in a table, then the input data for this method must
also be in a table.

`mar = margin(B,Xnew,Ynew)`

computes the
classification margins for the predictors contained in the matrix `Xnew`

given
true response `Ynew`

. If you trained `B`

using
sample data contained in a matrix, then the input data for this method
must also be in a matrix.

`Ynew`

can be a numeric vector, character matrix,
cell array of character vectors, categorical vector or logical vector. `mar`

is
a numeric array of size `Nobs`

-by-`NTrees`

,
where `Nobs`

is the number of rows of `TBLnew`

and `Ynew`

,
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,TBLnew,Ynew,'param1',val1,'param2',val2,...)`

or ```
mar
= margin(B,Xnew,Ynew,'param1',val1,'param2',val2,...)
```

specifies
optional parameter name-value pairs:

`'Mode'` | Character vector 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 from`trees(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. |

`'UseInstanceForTree'` | 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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