CompactTreeBagger class -

Compact ensemble of decision trees grown by bootstrap aggregation

Description

CompactTreeBagger class is a lightweight class that contains the trees grown using TreeBagger. CompactTreeBagger does not preserve any information about how TreeBagger grew the decision trees. It does not contain the input data used for growing trees, nor does it contain training parameters such as minimal leaf size or number of variables sampled for each decision split at random. You can only use CompactTreeBagger for predicting the response of the trained ensemble given new data X, and other related functions.

CompactTreeBagger lets you save the trained ensemble to disk, or use it in any other way, while discarding training data and various parameters of the training configuration irrelevant for predicting response of the fully grown ensemble. This reduces storage and memory requirements, especially for ensembles trained on large datasets.

Construction

CompactTreeBagger

Create CompactTreeBagger object

Methods

combine	Combine two ensembles
error	Error (misclassification probability or MSE)
margin	Classification margin
mdsProx	Multidimensional scaling of proximity matrix
meanMargin	Mean classification margin
outlierMeasure	Outlier measure for data
predict	Predict response
proximity	Proximity matrix for data
SetDefaultYfit	Set default value for `predict`

Properties

ClassNames	Names of classes
DefaultYfit	Default value returned by `predict`
DeltaCritDecisionSplit	Split criterion contributions for each predictor
Method	Method used by trees (classification or regression)
NTrees	Number of decision trees in ensemble
Trees	Decision trees in ensemble
VarNames	Variable names

Copy Semantics

Value. To learn how this affects your use of the class, see Comparing Handle and Value Classes in the MATLAB Object-Oriented Programming documentation.