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Classification edge for cross-validated classification model

returns the classification edge obtained by the cross-validated
classification model `E`

= kfoldEdge(`CVMdl`

)`CVMdl`

. For every fold,
`kfoldEdge`

computes the classification edge for validation-fold
observations using a classifier trained on training-fold observations.
`CVMdl.X`

and `CVMdl.Y`

contain both sets of
observations.

returns the classification edge with additional options specified by one or more name-value
arguments. For example, specify the folds to use or specify to compute the classification
edge for each individual fold.`E`

= kfoldEdge(`CVMdl`

,`Name,Value`

)

`kfoldEdge`

computes the classification edge as described in the
corresponding `edge`

object function. For a model-specific description, see
the appropriate `edge`

function reference page in the following
table.

Model Type | `edge` Function |
---|---|

Discriminant analysis classifier | `edge` |

Ensemble classifier | `edge` |

Generalized additive model classifier | `edge` |

k-nearest neighbor classifier | `edge` |

Naive Bayes classifier | `edge` |

Neural network classifier | `edge` |

Support vector machine classifier | `edge` |

Binary decision tree for multiclass classification | `edge` |

`ClassificationPartitionedModel`

| `kfoldfun`

| `kfoldLoss`

| `kfoldMargin`

| `kfoldPredict`