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**Package: **classreg.learning.partition

**Superclasses: **`ClassificationPartitionedModel`

Cross-validated linear model for binary classification of high-dimensional data

`ClassificationPartitionedLinear`

is a set of linear
classification models trained on cross-validated folds. To obtain a cross-validated,
linear classification model, use `fitclinear`

and specify one of the cross-validation options. You can
estimate the quality of classification, or how well the linear classification model
generalizes, using one or more of these “kfold” methods: `kfoldPredict`

, `kfoldLoss`

, `kfoldMargin`

, and `kfoldEdge`

.

Every “kfold” method uses models trained on in-fold observations to
predict the response for 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 roughly equally sized groups. The *training
fold* contains four of the groups (that is, roughly 4/5 of the data) and
the *test 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}`

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

`CVMdl.Trained{2}`

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

If you validate by calling `kfoldPredict`

, it computes predictions for
the observations in group 1 using the first model, group 2 for the second model, and so
on. In short, the software estimates a response for every observation using the model
trained without that observation.

`ClassificationPartitionedLinear`

model objects do
not store the predictor data set.

`CVMdl = fitclinear(X,Y,Name,Value)`

creates a cross-validated,
linear classification model when `Name`

is either
`'CrossVal'`

, `'CVPartition'`

,
`'Holdout'`

, or `'KFold'`

. For more details, see
`fitclinear`

.

kfoldEdge | Classification edge for observations not used for training |

kfoldLoss | Classification loss for observations not used in training |

kfoldMargin | Classification margins for observations not used in training |

kfoldPredict | Predict labels for observations not used for training |

Value. To learn how value classes affect copy operations, see Copying Objects (MATLAB).

`ClassificationLinear`

| `fitclinear`

| `kfoldLoss`

| `kfoldPredict`