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Cross-validated multiclass ECOC model for support vector machines (SVMs) and other classifiers

`ClassificationPartitionedECOC`

is a set of
error-correcting output codes (ECOC) models trained on cross-validated folds. Estimate
the quality of the cross-validated classification by using one or more
“kfold” functions: `kfoldPredict`

, `kfoldLoss`

, `kfoldMargin`

, `kfoldEdge`

, and `kfoldfun`

.

Every “kfold” method uses models trained on training-fold (in-fold)
observations to predict the response for validation-fold (out-of-fold) observations. For
example, suppose you cross-validate using five folds. In this case, the software
randomly assigns each observation into five groups of equal size (roughly). The
*training fold* contains four of the groups (roughly 4/5 of the
data), and the *validation fold* contains the other group (roughly
1/5 of the data). In this case, cross-validation proceeds as follows:

The software trains the first model (stored in

`CVMdl.Trained{1}`

) by using the observations in the last four groups and reserves the observations in the first group for validation.The software trains the second model (stored in

`CVMdl.Trained{2}`

) by using the observations in the first group and the last three groups. The software reserves the observations in the second group for validation.The software proceeds in a similar fashion for the third, fourth, and fifth models.

If you validate by using `kfoldPredict`

, the software computes
predictions for the observations in group *i* by using the
*i*th model. In short, the software estimates a response for every
observation by using the model trained without that observation.

You can create a `ClassificationPartitionedECOC`

model in two
ways:

`kfoldEdge` | Classification edge for cross-validated ECOC model |

`kfoldLoss` | Classification loss for cross-validated ECOC model |

`kfoldMargin` | Classification margins for cross-validated ECOC model |

`kfoldPredict` | Classify observations in cross-validated ECOC model |

`kfoldfun` | Cross-validate function using cross-validated ECOC model |

`ClassificationECOC`

| `CompactClassificationECOC`

| `crossval`

| `cvpartition`

| `fitcecoc`