RegressionLinear Class
Linear regression model for high-dimensional data
Description
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
.
Construction
Create a RegressionLinear
object by using fitrlinear
.
Properties
Object Functions
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 |
Copy Semantics
Value. To learn how value classes affect copy operations, see Copying Objects.