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Linear regression model for high-dimensional data

`RegressionLinear`

is a trained linear model object for regression; the linear model is a support vector machine regression (SVM) or linear regression model. `fitrlinear`

fits a `RegressionLinear`

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

Unlike other regression models, and for economical memory usage, `RegressionLinear`

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

You can use trained `RegressionLinear`

models to predict responses for new data. For details, see `predict`

.

Create a `RegressionLinear`

object by using `fitrlinear`

.

`incrementalLearner` | Convert linear regression model to incremental learner |

`lime` | Local interpretable model-agnostic explanations (LIME) |

`loss` | Regression loss for linear regression models |

`partialDependence` | Compute partial dependence |

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

`predict` | Predict response of linear regression model |

`selectModels` | Select fitted regularized linear regression models |

`shapley` | Shapley values |

`update` | Update model parameters for code generation |

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