Superclasses: CompactClassificationEnsemble
Ensemble classifier
ClassificationEnsemble
combines a set of trained
weak learner models and data on which these learners were trained.
It can predict ensemble response for new data by aggregating predictions
from its weak learners. It stores data used for training, can compute
resubstitution predictions, and can resume training if desired.
Create a classification ensemble object using fitcensemble
.

Indices of categorical
predictors, stored 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 

Numeric array of fit information. The 

Character vector describing the meaning of the 

Description of the crossvalidation optimization of hyperparameters,
stored as a


Cell array of character vectors with names of weak learners
in the ensemble. The name of each learner appears just once. For example,
if you have an ensemble of 100 trees, 

Character vector describing the method that creates 

Parameters used in training 

Numeric scalar containing the number of observations in the training data. 

Number of trained weak learners in 

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 describing the reason 

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 

Scaled 

Matrix of predictor values that trained the ensemble. Each column
of 

Numeric vector, categorical vector, logical vector, character
array, or cell array of character vectors. Each row of 
compact  Compact classification ensemble 
crossval  Cross validate ensemble 
resubEdge  Classification edge by resubstitution 
resubLoss  Classification error by resubstitution 
resubMargin  Classification margins by resubstitution 
resubPredict  Predict ensemble response by resubstitution 
resume  Resume training ensemble 
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).
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
})
ClassificationTree
 CompactClassificationEnsemble
 fitcensemble
 view