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Train Regression Ensemble

This example shows how to create a regression ensemble to predict mileage of cars based on their horsepower and weight, trained on the carsmall data.

Load the carsmall data set.

load carsmall

Prepare the predictor data.

X = [Horsepower Weight];

The response data is MPG. The only available boosted regression ensemble type is LSBoost. For this example, arbitrarily choose an ensemble of 100 trees, and use the default tree options.

Train an ensemble of regression trees.

Mdl = fitrensemble(X,MPG,'Method','LSBoost','NumLearningCycles',100)
Mdl = 
             ResponseName: 'Y'
    CategoricalPredictors: []
        ResponseTransform: 'none'
          NumObservations: 94
               NumTrained: 100
                   Method: 'LSBoost'
             LearnerNames: {'Tree'}
     ReasonForTermination: 'Terminated normally after completing the requested number of training cycles.'
                  FitInfo: [100x1 double]
       FitInfoDescription: {2x1 cell}
           Regularization: []

  Properties, Methods

Plot a graph of the first trained regression tree in the ensemble.


Figure Regression tree viewer contains an axes and other objects of type uimenu, uicontrol. The axes contains 36 objects of type line, text.

By default, fitrensemble grows shallow trees for LSBoost.

Predict the mileage of a car with 150 horsepower weighing 2750 lbs.

mileage = predict(Mdl,[150 2750])
mileage = 23.6713

See Also


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