Choose subset of multiclass ECOC models composed of binary
`ClassificationLinear`

learners

returns a subset of trained error-correcting output codes (ECOC) models composed of
`SubMdl`

= selectModels(`Mdl`

,`idx`

)`ClassificationLinear`

binary models from a set of multiclass
ECOC models (`Mdl`

) trained using various regularization
strengths. The indices (`idx`

) correspond to the regularization
strengths in `Mdl.BinaryLearners{1}.Lambda`

and specify which
models to return.

`SubMdl`

is returned as a `CompactClassificationECOC`

model object.

One way to build several predictive ECOC models composed of binary linear classification models is:

Create a linear classification model template using

`templateLinear`

and specify a grid of regularization strengths using the`'`

`Lambda`

`'`

name-value pair argument.Hold out a portion of the data for testing.

Train an ECOC model using

`fitcecoc`

. Specify the template using the`'`

`Learners`

`'`

name-value pair argument and supply the training data.`fitcecoc`

returns one`CompactClassificationECOC`

model object containing`ClassificationLinear`

binary learners, but all binary learners contain a model for each regularization strength.To determine the quality of each regularized model, pass the returned model object and the held-out data to, for example,

`loss`

.Identify the indices (

`idx`

) of a satisfactory subset of regularized models, and then pass the returned model and the indices to`selectModels`

. The function`selectModels`

returns one`CompactClassificationECOC`

model object, but it contains`numel(idx)`

regularized models.To predict class labels for new data, pass the data and the subset of regularized models to

`predict`

.

`ClassificationLinear`

| `CompactClassificationECOC`

| `fitcecoc`

| `loss`

| `predict`

| `templateLinear`