`out = outlierMeasure(B,X)`

out = outlierMeasure(B,X,'param1',val1,'param2',val2,...)

`out = outlierMeasure(B,X)`

computes outlier
measures for predictors `X`

using trees in the ensemble `B`

.
The method computes the outlier measure for a given observation by
taking an inverse of the average squared proximity between this observation
and other observations. `outlierMeasure`

then normalizes
these outlier measures by subtracting the median of their distribution,
taking the absolute value of this difference, and dividing by the
median absolute deviation. A high value of the outlier measure indicates
that this observation is an outlier.

You can supply the proximity matrix directly by using the `'data'`

parameter.

`out = outlierMeasure(B,X,'param1',val1,'param2',val2,...)`

specifies
optional parameter name/value pairs:

`'data'` | Flag indicating how to treat the `X` input
argument. If set to `'predictors'` (default), the
method assumes `X` is a matrix of predictors and
uses it for computation of the proximity matrix. If set to `'proximity'` ,
the method treats `X` as a proximity matrix returned
by the `proximity` method. If you do not supply the
proximity matrix, `outlierMeasure` computes it internally.
If you use the `proximity` method to compute a proximity
matrix, supplying it as input to `outlierMeasure` reduces
computing time. |

`'labels'` | Vector of true class labels. True class labels can be either
a numeric vector, character matrix, or cell array of strings. When
you supply this parameter, the method performs the outlier calculation
for any observations using only other observations from the same class.
This parameter must specify one label for each observation (row)
in `X` . |

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