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# meanMargin

Class: CompactTreeBagger

Mean classification margin

## Syntax

```mar = meanMargin(B,TBLnew,Ynew) mar = meanMargin(B,Xnew,Ynew) mar = meanMargin(B,TBLnew,Ynew,'param1',val1,'param2',val2,...) mar = meanMargin(B,Xnew,Ynew,'param1',val1,'param2',val2,...) ```

## Description

`mar = meanMargin(B,TBLnew,Ynew)` computes average classification margins for the predictors contained in the table `TBLnew` given the 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 = meanMargin(B,Xnew,Ynew)` computes average 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 either a numeric vector, character matrix, cell array of character vectors, categorical vector or logical vector. `meanMargin` averages the margins over all observations (rows) in `TBLnew` or `Xnew` for each tree. `mar` is a matrix of size 1-by-`NTrees`, where `NTrees` is the number of trees in the ensemble `B`. This method is available for classification ensembles only.

`mar = meanMargin(B,TBLnew,Ynew,'param1',val1,'param2',val2,...)` or ```mar = meanMargin(B,Xnew,Ynew,'param1',val1,'param2',val2,...)``` specifies optional parameter name-value pairs:

 `'Mode'` How `meanMargin` computes errors. If set to `'cumulative'` (default), is a vector of length `NTrees` where the first element gives mean margin from `trees(1)`, second column gives mean margins from `trees(1:2)` etc., up to `trees(1:NTrees)`. If set to `'individual'`, `mar` is a vector of length `NTrees`, where each element is a mean margin from each tree in the ensemble . If set to `'ensemble'`, `mar` is a scalar showing the cumulative mean margin 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 mean margin from `trees(1)`, the second element gives mean margin from `trees(1:2)` etc. `'TreeWeights'` Vector of tree weights. This vector must have the same length as the `'Trees'` vector. `meanMargin` 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.

## See Also

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