RegressionBaggedEnsemble class

Superclasses: RegressionEnsemble

Regression ensemble grown by resampling


RegressionBaggedEnsemble 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.


ens = fitensemble(tbl,ResponseVarName,'bag',nlearn,learners,'type','regression') creates a bagged regression ensemble. For more information on the syntax, see the fitensemble function reference page.

ens = fitensemble(tbl,formula,'bag',nlearn,learners,'type','regression') creates a bagged regression ensemble. For more information on the syntax, see the fitensemble function reference page.

ens = fitensemble(tbl,Y,'bag',nlearn,learners,'type','regression') creates a bagged regression ensemble. For more information on the syntax, see the fitensemble function reference page.

ens = fitensemble(X,Y,'bag',nlearn,learners,'type','regression') creates a bagged regression ensemble. For more information on the syntax, see the fitensemble function reference page.



List of categorical predictors. CategoricalPredictors is a numeric vector with indices from 1 to p, where p is the number of columns of X.


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 ExpandedPredictorNames includes the names that describe the expanded variables. Otherwise, ExpandedPredictorNames is the same as PredictorNames.


A numeric array of fit information. The FitInfoDescription property describes the content of this array.


String describing the meaning of the FitInfo array.


A numeric scalar between 0 and 1. FResample is the fraction of training data fitensemble resampled at random for every weak learner when constructing the ensemble.


Cell array of strings with names of the weak learners in the ensemble. The name of each learner appears just once. For example, if you have an ensemble of 100 trees, LearnerNames is {'Tree'}.


A string with the name of the algorithm fitensemble used for training the ensemble.


Parameters used in training ens.


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


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 X.


A string describing the reason fitensemble stopped adding weak learners to the ensemble.


A structure containing the result of the regularize method. Use Regularization with shrink to lower resubstitution error and shrink the ensemble.


Boolean flag indicating if training data for weak learners in this ensemble were sampled with replacement. Replace is true for sampling with replacement, false otherwise.


A string with the name of the response variable Y.


Function handle for transforming scores, or string representing a built-in transformation function. 'none' means no transformation; equivalently, 'none' means @(x)x.

Add or change a ResponseTransform function using dot notation:

ens.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.


A logical matrix of size N-by-NumTrained, where N is the number of rows (observations) in the training data X, and NumTrained is the number of trained weak learners. UseObsForLearner(I,J) is true if observation I was used for training learner J, and is false otherwise.


The scaled weights, a vector with length n, the number of rows in X. The sum of the elements of W is 1.


The matrix of predictor values that trained the ensemble. Each column of X represents one variable, and each row represents one observation.


The numeric column vector with the same number of rows as X that trained the ensemble. Each entry in Y is the response to the data in the corresponding row of X.


oobLossOut-of-bag regression error
oobPredictPredict out-of-bag response of ensemble

Inherited Methods

compactCreate compact regression ensemble
crossvalCross validate ensemble
cvshrinkCross validate shrinking (pruning) ensemble
regularizeFind weights to minimize resubstitution error plus penalty term
resubLossRegression error by resubstitution
resubPredictPredict response of ensemble by resubstitution
resumeResume training ensemble
shrinkPrune ensemble
lossRegression error
predictPredict response of ensemble
predictorImportanceEstimates of predictor importance
removeLearnersRemove members of compact regression ensemble

Copy Semantics

Value. To learn how value classes affect copy operations, see Copying Objects in the MATLAB® documentation.


Create a bagged regression ensemble to predict the mileage of cars in the carsmall data set based on their engine displacement, horsepower, and weight:

load carsmall
X = [Displacement Horsepower Weight];
ens = fitensemble(X,MPG,'bag',100,'Tree',...

ens = 

           PredictorNames: {'x1'  'x2'  'x3'}
    CategoricalPredictors: []
             ResponseName: 'Y'
        ResponseTransform: 'none'
            NumObservations: 94
                 NumTrained: 100
                   Method: 'Bag'
             LearnerNames: {'Tree'}
     ReasonForTermination: [1x77 char]
                  FitInfo: []
       FitInfoDescription: 'None'
           Regularization: []
                FResample: 1
                  Replace: 1
         UseObsForLearner: [94x100 logical]

Predict the mileage of a car whose characteristics are the average of those of the first 10 cars:

car10 = mean(X(1:10,:));

ans =
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