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

A classification ensemble created by 

List of categorical predictors. 

List of the elements in 

Character vector describing how 

Square matrix, where 

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

Number of trained weak learners in 

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

Numeric vector of prior probabilities for each class. The order
of the elements of 

Character vector with the name of the response variable 

Function handle for transforming scores, or character vector
representing a builtin transformation function. Add or change a ens.ScoreTransform = 'function' or ens.ScoreTransform = @function 

A cell vector of trained classification models.


Numeric vector of trained weights for the weak learners in 

Logical matrix of size If the ensemble is not of type 
compareHoldout  Compare accuracies of two classification models using new data 
edge  Classification edge 
loss  Classification error 
margin  Classification margins 
predict  Predict labels using ensemble of classification models 
predictorImportance  Estimates of predictor importance 
removeLearners  Remove members of compact classification ensemble 
Value. To learn how value classes affect copy operations, see Copying Objects (MATLAB) in the MATLAB^{®} documentation.
For an ensemble of classification trees, the Trained
property
of ens
stores an ens.NumTrained
by1
cell vector of compact classification models. For a textual or graphical
display of tree t
in the cell vector, enter:
view(ens.Trained{
for
ensembles aggregated using LogitBoost or GentleBoost.t
}.CompactRegressionLearner)
view(ens.Trained{
for
all other aggregation methods.t
})
ClassificationEnsemble
 compact
 fitcensemble
 fitctree
 predict
 view