Classification edge for observations not used for training
returns
the cross-validated classification
edges obtained by the cross-validated, error-correcting output
codes (ECOC) model composed of linear classification models e
= kfoldEdge(CVMdl
)CVMdl
.
That is, for every fold, kfoldEdge
estimates the
classification edge for observations that it holds out when it trains
using all other observations.
e
contains a classification edge for each
regularization strength in the linear classification models that comprise CVMdl
.
uses
additional options specified by one or more e
= kfoldEdge(CVMdl
,Name,Value
)Name,Value
pair
arguments. For example, specify a decoding scheme, which folds to
use for the edge calculation, or verbosity level.
[1] Allwein, E., R. Schapire, and Y. Singer. “Reducing multiclass to binary: A unifying approach for margin classifiers.” Journal of Machine Learning Research. Vol. 1, 2000, pp. 113–141.
[2] Escalera, S., O. Pujol, and P. Radeva. “On the decoding process in ternary error-correcting output codes.” IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 32, Issue 7, 2010, pp. 120–134.
[3] Escalera, S., O. Pujol, and P. Radeva. “Separability of ternary codes for sparse designs of error-correcting output codes.” Pattern Recogn. Vol. 30, Issue 3, 2009, pp. 285–297.
ClassificationECOC
| ClassificationLinear
| ClassificationPartitionedLinearECOC
| edge
| fitcecoc
| kfoldMargin
| kfoldPredict
| statset