Reduce size of regression ensemble model
You can predict regressions using the
cens in the same way as when you use
ens. However, because
not contain training data, you cannot perform some actions, such as
View Size of Compact Regression Ensemble
Compare the size of a regression ensemble for the
carsmall data to the size of the compact version of the ensemble.
carsmall data set and select acceleration, number of cylinders, displacement, horsepower, and vehicle weight as predictors.
load carsmall X = [Acceleration Cylinders Displacement Horsepower Weight];
Train an ensemble of regression trees.
ens = fitrensemble(X,MPG);
Create a compact version of
ens and compare ensemble sizes.
cens = compact(ens); b = whos("ens"); c = whos("cens"); [b.bytes c.bytes] % b.bytes = size of ens and c.bytes = size of cens
ans = 1×2 501081 468548
The compact ensemble uses less memory.
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 R2011a