Linear model for binary classification of high-dimensional data

`ClassificationLinear`

is a trained linear model object for binary
classification; the linear model is a support vector machine (SVM) or logistic
regression model. `fitclinear`

fits a
`ClassificationLinear`

model by minimizing the objective function
using techniques that reduce computation time for high-dimensional data sets (e.g.,
stochastic gradient descent). The classification loss plus the regularization term
compose the objective function.

Unlike other classification models, and for economical memory usage,
`ClassificationLinear`

model objects do not store the training
data. However, they do store, for example, the estimated linear model coefficients,
prior-class probabilities, and the regularization strength.

You can use trained `ClassificationLinear`

models to predict labels
or classification scores for new data. For details, see `predict`

.

Create a `ClassificationLinear`

object by using `fitclinear`

.

edge | Classification edge for linear classification models |

loss | Classification loss for linear classification models |

margin | Classification margins for linear classification models |

predict | Predict labels for linear classification models |

selectModels | Choose subset of regularized, binary linear classification models |

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

`ClassificationECOC`

| `ClassificationKernel`

| `ClassificationPartitionedLinear`

| `ClassificationPartitionedLinearECOC`

| `fitclinear`

| `predict`