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Cross-validated, binary kernel classification model

`ClassificationPartitionedKernel`

is a binary kernel classification
model, trained on cross-validated folds. You can estimate the quality of classification, or
how well the kernel classification model generalizes, using one or more “kfold”
functions: `kfoldPredict`

,
`kfoldLoss`

,
`kfoldMargin`

, and
`kfoldEdge`

.

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 that 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 (that is, roughly 4/5 of the data) and the
*validation fold* contains the other group (that is, 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}`

) 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.

`ClassificationPartitionedKernel`

model objects do not store the
predictor data set.

You can create a `ClassificationPartitionedKernel`

model by training a
classification kernel model using `fitckernel`

and
specifying one of these name-value pair arguments: `'Crossval'`

,
`'CVPartition'`

, `'Holdout'`

, `'KFold'`

,
or `'Leaveout'`

.

`kfoldEdge` | Classification edge for cross-validated kernel classification model |

`kfoldLoss` | Classification loss for cross-validated kernel classification model |

`kfoldMargin` | Classification margins for cross-validated kernel classification model |

`kfoldPredict` | Classify observations in cross-validated kernel classification model |