# selectModels

**Class: **ClassificationLinear

Choose subset of regularized, binary linear classification models

## Description

returns
a subset of trained, binary linear classification models from a set
of binary linear classification models (`SubMdl`

= selectModels(`Mdl`

,`idx`

)`Mdl`

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

)
correspond to the regularization strengths in `Mdl.Lambda`

,
and specify which models to return.

## Input Arguments

## Output Arguments

## Examples

## Tips

One way to build several predictive, binary linear classification models is:

Hold out a portion of the data for testing.

Train a binary, linear classification model using

`fitclinear`

. Specify a grid of regularization strengths using the`'`

`Lambda`

`'`

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

returns one`ClassificationLinear`

model object, but it contains 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`

.`selectModels`

returns one`ClassificationLinear`

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`

.

## See Also

**Introduced in R2016a**