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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
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263–286.

[3] Escalera, S., O. Pujol, and P. Radeva.
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[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*.
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[6] Wu, T. F., C. J. Lin, and R. Weng. “Probability
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[7] Zadrozny, B. “Reducing Multiclass
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`ClassificationECOC`

| `ClassificationLinear`

| `ClassificationPartitionedLinearECOC`

| `confusionchart`

| `fitcecoc`

| `perfcurve`

| `predict`

| `statset`

| `testcholdout`