Compact generalized linear regression model
returns the compact generalized linear regression model
compactMdl = compact(
compactMdl, which is the compact version of the full, fitted
generalized linear regression model
Compact Generalized Linear Regression Model
Fit a generalized linear regression model to data and reduce the size of a full, fitted model by discarding the sample data and some information related to the fitting process.
largedata4reg data set, which contains 15,000 observations and 45 predictor variables.
Fit a generalized linear regression model to the data using the first 15 predictor variables.
mdl = fitglm(X(:,1:15),Y);
Compact the model.
compactMdl = compact(mdl);
The compact model discards the original sample data and some information related to the fitting process, so it uses less memory than the full model.
Compare the size of the full model
mdl and the compact model
vars = whos('compactMdl','mdl'); [vars(1).bytes,vars(2).bytes]
ans = 1×2 15518 4382501
The compact model consumes less memory than the full model.
compactMdl — Compact generalized linear regression model
Compact generalized linear regression model, returned as a
CompactGeneralizedLinearModel object consumes less
memory than a
GeneralizedLinearModel object because a
compact model does not store the input data used to fit the model or
information related to the fitting process. You can still use a compact
model to predict responses using new input data, but some
GeneralizedLinearModel object functions that require
the input data do not work with a compact model.
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.
This function fully supports GPU arrays. For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).
Introduced in R2016b