Compact regression ensemble class
Compact version of a regression ensemble (of class RegressionEnsemble
).
The compact version does not include the data for training the regression
ensemble. Therefore, you cannot perform some tasks with a compact
regression ensemble, such as cross validation. Use a compact regression
ensemble for making predictions (regressions) of new data.
constructs a compact
decision ensemble from a full decision ensemble.cens
=
compact(ens
)

A regression ensemble created by 

List of categorical predictors. 

A string describing how the ensemble combines learner predictions. 

Expanded predictor names, stored as a cell array of strings. If the model uses encoding for categorical variables, then 

Number of trained learners in the ensemble, a positive scalar. 

A cell array of names for the predictor variables, in the order
in which they appear in 

A string with the name of the response variable 

Function handle for transforming scores, or string representing
a builtin transformation function. Add or change a cens.ResponseTransform = @function 

The trained learners, a cell array of compact regression models. 

A numeric vector of weights the ensemble assigns to its learners. The ensemble computes predicted response by aggregating weighted predictions from its learners. 
loss  Regression error 
predict  Predict response of ensemble 
predictorImportance  Estimates of predictor importance 
removeLearners  Remove members of compact regression ensemble 
Value. To learn how value classes affect copy operations, see Copying Objects in the MATLAB^{®} documentation.
For a compact ensemble of regression trees, the Trained
property
of cens
stores a cell vector of cens.NumTrained
CompactRegressionTree
model
objects. For a textual or graphical display of tree t
in
the cell vector, enter
view(cens.Trained{t})
compact
 fitensemble
 predict
 RegressionEnsemble
 templateTree
 view