Create a compact bag of trees for efficiently making predictions on new data.

Load theionospheredata set.

loadionosphere

Train a bag of 100 classification trees using all measurements and theAdaBoostM1method.

Mdl = TreeBagger(100,X,Y,'Method','classification')

Mdl = TreeBagger Ensemble with 100 bagged decision trees: Training X: [351x34] Training Y: [351x1] Method: classification NumPredictors: 34 NumPredictorsToSample: 6 MinLeafSize: 1 InBagFraction: 1 SampleWithReplacement: 1 ComputeOOBPrediction: 0 ComputeOOBPredictorImportance: 0 Proximity: [] ClassNames: 'b' 'g' Properties, Methods

Mdlis aTreeBaggermodel object that contains the training data, among other things.

Create a compact version ofMdl.

CMdl = compact(Mdl)

CMdl = CompactTreeBagger Ensemble with 100 bagged decision trees: Method: classification NumPredictors: 34 ClassNames: 'b' 'g' Properties, Methods

CMdlis aCompactTreeBaggermodel object.CMdlis almost the same asMdl. One exception is that it does not store the training data.

Compare the amounts of space consumed byMdlandCMdl.

mdlInfo = whos('Mdl'); cMdlInfo = whos('CMdl'); [mdlInfo.bytes cMdlInfo.bytes]

ans =1×21115742 976936

Mdlconsumes more space thanCMdl.

CMdl.Treesstores the trained classification trees (CompactClassificationTreemodel objects) that composeMdl.

显示一个图的第一棵树the compact model.

view(CMdl.Trees{1},“模式”,'graph');

By default,TreeBaggergrows deep trees.

Predict the label of the mean ofXusing the compact ensemble.

predMeanX = predict(CMdl,mean(X))

predMeanX =1x1 cell array{'g'}