Classify observations in multiclass error-correcting output codes (ECOC) model
returns a vector of predicted class labels (label
= resubPredict(Mdl
)label
) for the trained
multiclass error-correcting output codes (ECOC) model Mdl
using the
predictor data stored in Mdl.X
.
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
= resubPredict(Mdl
,Name,Value
)
[
uses any of the input argument combinations in the previous syntaxes and additionally
returns the negated average binary loss per class
(label
,NegLoss
,PBScore
]
= resubPredict(___)NegLoss
) for observations, and the positive-class scores
(PBScore
) for the observations classified by each binary
learner.
[
additionally returns posterior class probability estimates for observations
(label
,NegLoss
,PBScore
,Posterior
]
= resubPredict(___)Posterior
).
To obtain posterior class probabilities, you must set
'FitPosterior',true
when training the ECOC model using fitcecoc
. Otherwise, resubPredict
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.
ClassificationECOC
| fitcecoc
| predict
| resubLoss
| statset
| quadprog
(Optimization Toolbox)