Classify observations in cross-validated kernel ECOC model
returns class labels predicted by the cross-validated kernel ECOC model (label
= kfoldPredict(CVMdl
)ClassificationPartitionedKernelECOC
) CVMdl
. For every fold,
kfoldPredict
predicts class labels for validation-fold observations
using a model trained on training-fold observations. kfoldPredict
applies the same data used to create CVMdl
(see fitcecoc
).
The software predicts the classification of an observation by assigning the observation to the class yielding the largest negated average binary loss (or, equivalently, the smallest average binary loss).
returns predicted class labels with additional options specified by one or more name-value
pair arguments. For example, specify the posterior probability estimation method, decoding
scheme, or verbosity level.label
= kfoldPredict(CVMdl
,Name,Value
)
[
additionally returns negated values of the average binary loss per class
(label
,NegLoss
,PBScore
]
= kfoldPredict(___)NegLoss
) for validation-fold observations and positive-class scores
(PBScore
) for validation-fold observations classified by each binary
learner, using any of the input argument combinations in the previous syntaxes.
If the coding matrix varies across folds (that is, the coding scheme is
sparserandom
or denserandom
), then
PBScore
is empty ([]
).
[
additionally returns posterior class probability estimates for validation-fold observations
(label
,NegLoss
,PBScore
,Posterior
]
= kfoldPredict(___)Posterior
).
To obtain posterior class probabilities, the kernel classification binary learners must
be logistic regression models. Otherwise, kfoldPredict
throws an
error.
[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] 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.