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`

.

loss | Regression loss for linear regression models |

predict | Predict response of linear regression model |

selectModels | Select fitted regularized linear regression models |

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

`RegressionPartitionedLinear`

| `fitrlinear`

| `plotPartialDependence`

| `predict`