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Compact multiclass model for support vector machines (SVMs) and other classifiers

`CompactClassificationECOC`

is a compact version of the multiclass
error-correcting output codes (ECOC) model. The compact classifier does not include the data
used for training the multiclass ECOC model. Therefore, you cannot perform certain tasks, such
as cross-validation, using the compact classifier. Use a compact multiclass ECOC model for
tasks such as classifying new data (`predict`

).

You can create a `CompactClassificationECOC`

model in two ways:

Create a compact ECOC model from a trained

`ClassificationECOC`

model by using the`compact`

object function.Create a compact ECOC model by using the

`fitcecoc`

function and specifying the`'Learners'`

name-value pair argument as`'linear'`

,`'kernel'`

, a`templateLinear`

or`templateKernel`

object, or a cell array of such objects.

`compareHoldout` | Compare accuracies of two classification models using new data |

`discardSupportVectors` | Discard support vectors of linear SVM binary learners in ECOC model |

`edge` | Classification edge for multiclass error-correcting output codes (ECOC) model |

`gather` | Gather properties of Statistics and Machine Learning Toolbox object from GPU |

`lime` | Local interpretable model-agnostic explanations (LIME) |

`loss` | Classification loss for multiclass error-correcting output codes (ECOC) model |

`margin` | Classification margins for multiclass error-correcting output codes (ECOC) model |

`partialDependence` | Compute partial dependence |

`plotPartialDependence` | Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots |

`predict` | Classify observations using multiclass error-correcting output codes (ECOC) model |

`shapley` | Shapley values |

`selectModels` | Choose subset of multiclass ECOC models composed of binary
`ClassificationLinear` learners |

`update` | Update model parameters for code generation |

[1] Fürnkranz, Johannes. “Round Robin
Classification.” *Journal of Machine Learning Research*, Vol. 2,
2002, pp. 721–747.

[2] Escalera, S., O. Pujol, and P. Radeva. “Separability of
ternary codes for sparse designs of error-correcting output codes.” *Pattern
Recognition Letters*, Vol. 30, Issue 3, 2009, pp. 285–297.

`ClassificationECOC`

| `fitcecoc`

| `compact`

| `ClassificationPartitionedLinearECOC`

| `ClassificationPartitionedKernelECOC`

| `ClassificationPartitionedECOC`