Classification loss for observations not used in training
returns
the cross-validated classification
error rates estimated by the cross-validated, error-correcting
output codes (ECOC) model composed of linear classification models L
= kfoldLoss(CVMdl
)CVMdl
.
That is, for every fold, kfoldLoss
estimates the
classification error rate for observations that it holds out when
it trains using all other observations. kfoldLoss
applies
the same data used create CVMdl
(see fitcecoc
).
L
contains a classification loss for each
regularization strength in the linear classification models that compose CVMdl
.
uses
additional options specified by one or more L
= kfoldLoss(CVMdl
,Name,Value
)Name,Value
pair
arguments. For example, specify a decoding scheme, which folds to
use for the loss 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
| fitcecoc
| kfoldPredict
| loss
| statset