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 |

`incrementalLearner` | Convert linear model for binary classification to incremental learner |

`loss` | Classification loss for linear classification models |

`margin` | Classification margins for linear classification models |

`partialDependence` | Compute partial dependence |

`plotPartialDependence` | Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots |

`predict` | Predict labels for linear classification models |

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

`update` | Update model parameters for code generation |

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

`ClassificationECOC`

| `ClassificationKernel`

| `ClassificationPartitionedLinear`

| `ClassificationPartitionedLinearECOC`

| `fitclinear`

| `predict`