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Compact classification ensemble


Compact version of a classification ensemble. 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.




ens = compact(fullEns) constructs a compact decision ensemble from a full decision ensemble.

Input Arguments

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Classification ensemble object, specified as the output of fitcensemble.


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This property is read-only.

Categorical predictor indices, specified as a vector of positive integers. CategoricalPredictors contains index values indicating that the corresponding predictors are categorical. The index values are between 1 and p, where p is the number of predictors used to train the model. If none of the predictors are categorical, then this property is empty ([]).

Data Types: single | double

This property is read-only.

List of the elements in Y with duplicates removed, returned as a categorical array, cell array of character vectors, character array, logical vector, or a numeric vector. ClassNames has the same data type as the data in the argument Y. (The software treats string arrays as cell arrays of character vectors.)

Data Types: double | logical | char | cell | categorical

This property is read-only.

How the ensemble combines weak learner weights, returned as either 'WeightedAverage' or 'WeightedSum'.

Data Types: char

Cost of classifying a point into class j when its true class is i, returned as a square matrix. The rows of Cost correspond to the true class and the columns correspond to the predicted class. The order of the rows and columns of Cost corresponds to the order of the classes in ClassNames. The number of rows and columns in Cost is the number of unique classes in the response.

Data Types: double

This property is read-only.

Expanded predictor names, returned as a cell array of character vectors.

If the model uses encoding for categorical variables, then ExpandedPredictorNames includes the names that describe the expanded variables. Otherwise, ExpandedPredictorNames is the same as PredictorNames.

Data Types: cell

This property is read-only.

Number of trained weak learners in the ensemble, returned as a positive integer.

Data Types: double

This property is read-only.

Predictor names, specified as a cell array of character vectors. The order of the entries in PredictorNames is the same as in the training data.

Data Types: cell

Prior probabilities for each class, returned as an m-element vector, where m is the number of unique classes in the response. The order of the elements of Prior corresponds to the order of the classes in ClassNames.

Data Types: double

This property is read-only.

Name of the response variable, returned as a character vector.

Data Types: char

Function for transforming scores, specified as a function handle or the name of a built-in transformation function. 'none' means no transformation; equivalently, 'none' means @(x)x. For a list of built-in transformation functions and the syntax of custom transformation functions, see fitctree.

Add or change a ScoreTransform function using dot notation:

ctree.ScoreTransform = 'function'
% or
ctree.ScoreTransform = @function

Data Types: char | string | function_handle

Trained classification models, returned as a cell vector. The entries of the cell vector contain the corresponding compact classification models.

Data Types: cell

This property is read-only.

Trained weights for the weak learners in the ensemble, returned as a numeric vector. TrainedWeights has T elements, where T is the number of weak learners in learners. The ensemble computes predicted response by aggregating weighted predictions from its learners.

Data Types: double

Indicator that learner j uses predictor i, returned as a logical matrix of size P-by-NumTrained, where P is the number of predictors (columns) in the training data. UsePredForLearner(i,j) is true when learner j uses predictor i, and is false otherwise. For each learner, the predictors have the same order as the columns in the training data.

If the ensemble is not of type Subspace, all entries in UsePredForLearner are true.

Data Types: logical

Object Functions

compareHoldoutCompare accuracies of two classification models using new data
edgeClassification edge for classification ensemble model
gatherGather properties of Statistics and Machine Learning Toolbox object from GPU
limeLocal interpretable model-agnostic explanations (LIME)
lossClassification loss for classification ensemble model
marginClassification margins for classification ensemble model
partialDependenceCompute partial dependence
plotPartialDependenceCreate partial dependence plot (PDP) and individual conditional expectation (ICE) plots
predictPredict labels using classification ensemble model
predictorImportanceEstimates of predictor importance for classification ensemble of decision trees
removeLearnersRemove members of compact classification ensemble
shapleyShapley values


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Create a compact classification ensemble for efficiently making predictions on new data.

Load the ionosphere data set.

load ionosphere

Train a boosted ensemble of 100 classification trees using all measurements and the AdaBoostM1 method.

Mdl = fitcensemble(X,Y,Method="AdaBoostM1")
Mdl = 
             ResponseName: 'Y'
    CategoricalPredictors: []
               ClassNames: {'b'  'g'}
           ScoreTransform: 'none'
          NumObservations: 351
               NumTrained: 100
                   Method: 'AdaBoostM1'
             LearnerNames: {'Tree'}
     ReasonForTermination: 'Terminated normally after completing the requested number of training cycles.'
                  FitInfo: [100x1 double]
       FitInfoDescription: {2x1 cell}

Mdl is a ClassificationEnsemble model object that contains the training data, among other things.

Create a compact version of Mdl.

CMdl = compact(Mdl)
CMdl = 
             ResponseName: 'Y'
    CategoricalPredictors: []
               ClassNames: {'b'  'g'}
           ScoreTransform: 'none'
               NumTrained: 100

CMdl is a CompactClassificationEnsemble model object. CMdl is almost the same as Mdl. One exception is that CMdl does not store the training data.

Compare the amounts of space consumed by Mdl and CMdl.

mdlInfo = whos("Mdl");
cMdlInfo = whos("CMdl");
[mdlInfo.bytes cMdlInfo.bytes]
ans = 1×2

      895597      648755

Mdl consumes more space than CMdl.

CMdl.Trained stores the trained classification trees (CompactClassificationTree model objects) that compose Mdl.

Display a graph of the first tree in the compact ensemble.


By default, fitcensemble grows shallow trees for boosted ensembles of trees.

Predict the label of the mean of X using the compact ensemble.

predMeanX = predict(CMdl,mean(X))
predMeanX = 1x1 cell array


For an ensemble of classification trees, the Trained property of ens stores an ens.NumTrained-by-1 cell vector of compact classification models. For a textual or graphical display of tree t in the cell vector, enter:

  • view(ens.Trained{t}.CompactRegressionLearner) for ensembles aggregated using LogitBoost or GentleBoost.

  • view(ens.Trained{t}) for all other aggregation methods.

Extended Capabilities

Version History

Introduced in R2011a

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