oobMeanMargin

Out-of-bag mean margins

Syntax

mar = oobMeanMargin(B)
mar = oobMeanMargin(B,'param1',val1,'param2',val2,...)


Description

mar = oobMeanMargin(B) computes average classification margins for out-of-bag observations in the training data, using the trained bagger B. oobMeanMargin averages the margins over all out-of-bag observations. mar is a row-vector of length NTrees, where NTrees is the number of trees in the ensemble.

mar = oobMeanMargin(B,'param1',val1,'param2',val2,...) specifies optional parameter name/value pairs:

'Mode'	Character vector or string scalar indicating how oobMeanMargin 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. oobMeanMargin 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.

Version History

R2022a: oobMeanMargin returns a different value for a model with a nondefault cost matrix

If you specify a nondefault cost matrix when you train the input model object, the oobMeanMargin function returns a different value compared to previous releases.

The oobMeanMargin function uses the observation weights stored in the W property. The way the function uses the W property value has not changed. However, the property value stored in the input model object has changed for a model with a nondefault cost matrix, so the function might return a different value.

For details about the property value change, see Cost property stores the user-specified cost matrix.

If you want the software to handle the cost matrix, prior probabilities, and observation weights in the same way as in previous releases, adjust the prior probabilities and observation weights for the nondefault cost matrix, as described in Adjust Prior Probabilities and Observation Weights for Misclassification Cost Matrix. Then, when you train a classification model, specify the adjusted prior probabilities and observation weights by using the Prior and Weights name-value arguments, respectively, and use the default cost matrix.