Package: classreg.learning.classif
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 

Categorical predictor
indices, specified as a vector of positive integers. 

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 
edge  Classification edge 
loss  Classification error 
margin  Classification margins 
predict  Classify observations 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).
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
 compareHoldout
 fitcensemble
 fitctree
 predict
 view