Train Classification Ensemble

This example shows how to create a classification tree ensemble for the ionosphere data set, and use it to predict the classification of a radar return with average measurements.

Load the ionosphere data set.

load ionosphere

Train a classification ensemble. For binary classification problems, fitcensemble aggregates 100 classification trees using LogitBoost.

Mdl = fitcensemble(X,Y)

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

Mdl is a ClassificationEnsemble model.

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

view(Mdl.Trained{1}.CompactRegressionLearner,'Mode','graph');

By default, fitcensemble grows shallow trees for boosting algorithms. You can alter the tree depth by passing a tree template object to fitcensemble. For more details, see templateTree.

Predict the quality of a radar return with average predictor measurements.

label = predict(Mdl,mean(X))

label = 1x1 cell array
    {'g'}

See Also

fitcensemble | predict

Related Topics

• Train Regression Ensemble
• Select Predictors for Random Forests
• Decision Trees
• Ensemble Algorithms
• Framework for Ensemble Learning