a compact version of
CMdl = compact(
TreeBagger model object. You can predict
CMdl exactly as you can using
CMdl does not contain training data,
you cannot perform some actions, such as make out-of-bag predictions
A regression ensemble created with
A compact regression ensemble.
Create a compact bag of trees for efficiently making predictions on new data.
ionosphere data set.
Train a bag of 100 classification trees using all measurements and the
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'
Mdl is a
TreeBagger model object that contains the training data, among other things.
Create a compact version of
CMdl = compact(Mdl)
CMdl = CompactTreeBagger Ensemble with 100 bagged decision trees: Method: classification NumPredictors: 34 ClassNames: 'b' 'g'
CMdl is a
CompactTreeBagger model object.
CMdl is almost the same as
Mdl. One exception is that it does not store the training data.
Compare the amounts of space consumed by
mdlInfo = whos('Mdl'); cMdlInfo = whos('CMdl'); [mdlInfo.bytes cMdlInfo.bytes]
ans = 1971143 1829948
Mdl consumes more space than
CMdl.Trees stores the trained classification trees (
CompactClassificationTree model objects) that compose
Display a graph of the first tree in the compact model.
TreeBagger grows deep trees.
Predict the label of the mean of
X using the compact ensemble.
predMeanX = predict(CMdl,mean(X))
predMeanX = cell 'g'