Classify observations in cross-validated ECOC model
returns class labels predicted by the cross-validated ECOC model (label
= kfoldPredict(CVMdl
)ClassificationPartitionedECOC
) CVMdl
. For every
fold, kfoldPredict
predicts class labels for observations that
it holds out during training. CVMdl.X
contains both sets of
observations.
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, you must set
'FitPosterior',1
when training the cross-validated ECOC model
using fitcecoc
. 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.
ClassificationECOC
| ClassificationPartitionedECOC
| edge
| fitcecoc
| predict
| statset
| quadprog
(Optimization Toolbox)