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TreeBagger - Class: TreeBagger

Create ensemble of bagged decision trees

Syntax

B = TreeBagger(ntrees,X,Y)
B = TreeBagger(ntrees,X,Y,'param1',val1,'param2',val2,...)

Description

B = TreeBagger(ntrees,X,Y) creates an ensemble B of ntrees decision trees for predicting response Y as a function of predictors X. By default TreeBagger builds an ensemble of classification trees. The function can build an ensemble of regression trees by setting the optional input argument 'method' to 'regression'.

X is a numeric matrix of training data. Each row represents an observation and each column represents a predictor or feature. Y is an array of true class labels for classification or numeric function values for regression. True class labels can be a numeric vector, character matrix, vector cell array of strings or categorical vector. TreeBagger converts labels to a cell array of strings for classification.

For more information on grouping variables, see Grouped Data.

B = TreeBagger(ntrees,X,Y,'param1',val1,'param2',val2,...) specifies optional parameter name/value pairs:

'FBoot'Fraction of input data to sample with replacement from the input data for growing each new tree. Default value is 1.
'OOBPred''on' to store info on what observations are out of bag for each tree. This info can be used by oobPredict to compute the predicted class probabilities for each tree in the ensemble. Default is 'off'.
'OOBVarImp''on' to store out-of-bag estimates of feature importance in the ensemble. Default is 'off'. Specifying 'on' also sets the 'OOBPred' value to 'on'.
'Method'Either 'classification' or 'regression'. Regression requires a numeric Y.
'NVarToSample'Number of variables to select at random for each decision split. Default is the square root of the number of variables for classification and one third of the number of variables for regression. Valid values are 'all' or a positive integer. Setting this argument to any valid value but 'all' invokes Breiman's 'random forest' algorithm.
'NPrint'Number of training cycles (grown trees) after which TreeBagger displays a diagnostic message showing training progress. Default is no diagnostic messages.
'MinLeaf'Minimum number of observations per tree leaf. Default is 1 for classification and 5 for regression.
'Options'A structure that specifies options that govern the computation when growing the ensemble of decision trees. One option requests that the computation of decision trees on multiple bootstrap replicates uses multiple processors, if the Parallel Computing Toolbox is available. Two options specify the random number streams to use in selecting bootstrap replicates. You can create this argument with a call to statset. You can retrieve values of the individual fields with a call to statget. Applicable statset parameters are:

  • 'UseParallel' — If 'always' and if a matlabpool of the Parallel Computing Toolbox is open, compute decision trees drawn on separate boostrap replicates in parallel. If the Parallel Computing Toolbox is not installed, or a matlabpool is not open, computation occurs in serial mode. Default is 'never', or serial computation.

  • 'UseSubstreams' — If 'always' select each bootstrap replicate using a separate Substream of the random number generator (aka Stream). This option is available only with RandStream types that support Substreams: 'mlfg6331_64' or 'mrg32k3a'. Default is 'never', do not use a different Substream to compute each bootstrap replicate.

  • Streams — A RandStream object or cell array of such objects. If you do not specify Streams, TreeBagger uses the default stream or streams. If you choose to specify Streams, use a single object except in the case

    • You have an open MATLAB pool

    • UseParallel is 'always'

    • UseSubstreams is 'never'

    In that case, use a cell array the same size as the MATLAB pool.

For more information on using parallel computing, see Parallel Statistics.

In addition to the optional arguments above, this method accepts all optional classregtree arguments with the exception of 'minparent'. Refer to the documentation for classregtree for more detail.

Examples

load fisheriris
b = TreeBagger(50,meas,species,'OOBPred','on')
plot(oobError(b))
xlabel('number of grown trees')
ylabel('out-of-bag classification error')

returns

b = 

Ensemble with 50 bagged decision trees:
               Training X:              [150x4]
               Training Y:              [150x1]
                   Method:       classification
                    Nvars:                    4
             NVarToSample:                    2
                  MinLeaf:                    1
                    FBoot:                    1
    SampleWithReplacement:                    1
     ComputeOOBPrediction:                    1
         ComputeOOBVarImp:                    0
                Proximity:                   []
                    Prune:                    0
              MergeLeaves:                    0
                 TreeArgs:
               ClassNames:'setosa' 'versicolor' 'virginica'

See Also

classregtree | CompactTreeBagger

How To

  

 

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