Predict labels for observations not used for training
returns
class labels predicted by the cross-validated ECOC model composed
of linear classification models Label
= kfoldPredict(CVMdl
)CVMdl
. That is,
for every fold, kfoldPredict
predicts class labels
for observations that it holds out when it trains using all other
observations. kfoldPredict
applies the same data
used create CVMdl
(see fitcecoc
).
Also, Label
contains class labels for each
regularization strength in the linear classification models that compose CVMdl
.
returns
predicted class labels with additional options specified by one or
more Label
= kfoldPredict(CVMdl
,Name,Value
)Name,Value
pair arguments. For example,
specify the posterior probability estimation method, decoding scheme,
or verbosity level.
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[2] Dietterich, T., and G. Bakiri. “Solving Multiclass Learning Problems Via Error-Correcting Output Codes.” Journal of Artificial Intelligence Research. Vol. 2, 1995, pp. 263–286.
[3] 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.
[4] 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.
[5] Hastie, T., and R. Tibshirani. “Classification by Pairwise Coupling.” Annals of Statistics. Vol. 26, Issue 2, 1998, pp. 451–471.
[6] Wu, T. F., C. J. Lin, and R. Weng. “Probability Estimates for Multi-Class Classification by Pairwise Coupling.” Journal of Machine Learning Research. Vol. 5, 2004, pp. 975–1005.
[7] Zadrozny, B. “Reducing Multiclass to Binary by Coupling Probability Estimates.” NIPS 2001: Proceedings of Advances in Neural Information Processing Systems 14, 2001, pp. 1041–1048.
ClassificationECOC
| ClassificationLinear
| ClassificationPartitionedLinearECOC
| confusionchart
| fitcecoc
| perfcurve
| predict
| statset
| testcholdout