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| R2012a Documentation → Statistics Toolbox | |
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| Contents | Index |
Bootstrap aggregation for ensemble of decision trees
TreeBagger bags an ensemble of decision trees for either classification or regression. Bagging stands for bootstrap aggregation. Every tree in the ensemble is grown on an independently drawn bootstrap replica of input data. Observations not included in this replica are "out of bag" for this tree. To compute prediction of an ensemble of trees for unseen data, TreeBagger takes an average of predictions from individual trees. To estimate the prediction error of the bagged ensemble, you can compute predictions for each tree on its out-of-bag observations, average these predictions over the entire ensemble for each observation and then compare the predicted out-of-bag response with the true value at this observation.
TreeBagger relies on the classregtree functionality for growing individual trees. In particular, classregtree accepts the number of features selected at random for each decision split as an optional input argument.
The Compact property contains another class, CompactTreeBagger, with sufficient information to make predictions using new data. This information includes the tree ensemble, variable names, and class names (for classification). CompactTreeBagger requires less memory than TreeBagger, but only TreeBagger has methods for growing more trees for the ensemble. Once you grow an ensemble of trees using TreeBagger and no longer need access to the training data, you can opt to work with the compact version of the trained ensemble from then on.
| TreeBagger | Create ensemble of bagged decision trees |
| append | Append new trees to ensemble |
| compact | Compact ensemble of decision trees |
| error | Error (misclassification probability or MSE) |
| fillProximities | Proximity matrix for training data |
| growTrees | Train additional trees and add to ensemble |
| margin | Classification margin |
| mdsProx | Multidimensional scaling of proximity matrix |
| meanMargin | Mean classification margin |
| oobError | Out-of-bag error |
| oobMargin | Out-of-bag margins |
| oobMeanMargin | Out-of-bag mean margins |
| oobPredict | Ensemble predictions for out-of-bag observations |
| predict | Predict response |
ClassNames |
A cell array containing the class names for the response variable Y. This property is empty for regression trees. |
ComputeOOBPrediction |
A logical flag specifying whether out-of-bag predictions for training observations should be computed. The default is false. If this flag is true, the following properties are available:
If this flag is true, the following methods can be called:
See also oobError, OOBIndices, OOBInstanceWeight, oobMargin, oobMeanMargin. |
Cost |
A matrix with misclassification costs. This property is empty for ensembles of regression trees. See also classregtree. |
DeltaCritDecisionSplit |
A numeric array of size 1-by-Nvars of changes in the split criterion summed over splits on each variable, averaged across the entire ensemble of grown trees. See also classregtree.varimportance. |
NVarSplit |
A numeric array of size 1-by-Nvars, where every element gives a number of splits on this predictor summed over all trees. |
OOBPermutedVarDeltaError |
A numeric array of size 1-by-Nvars containing a measure of importance for each predictor variable (feature). For any variable, the measure is the increase in prediction error if the values of that variable are permuted across the out-of-bag observations. This measure is computed for every tree, then averaged over the entire ensemble and divided by the standard deviation over the entire ensemble. |
OOBPermutedVarDeltaMeanMargin |
A numeric array of size 1-by-Nvars containing a measure of importance for each predictor variable (feature). For any variable, the measure is the decrease in the classification margin if the values of that variable are permuted across the out-of-bag observations. This measure is computed for every tree, then averaged over the entire ensemble and divided by the standard deviation over the entire ensemble. This property is empty for regression trees. |
OutlierMeasure |
A numeric array of size Nobs-by-1, where Nobs is the number of observations in the training data, containing outlier measures for each observation. See also CompactTreeBagger.OutlierMeasure. |
Prior |
A vector with prior probabilities for classes. This property is empty for ensembles of regression trees. See also classregtree. |
Proximity |
A numeric matrix of size Nobs-by-Nobs, where Nobs is the number of observations in the training data, containing measures of the proximity between observations. For any two observations, their proximity is defined as the fraction of trees for which these observations land on the same leaf. This is a symmetric matrix with 1s on the diagonal and off-diagonal elements ranging from 0 to 1. See also CompactTreeBagger.proximity, classregtree.varimportance. |
Prune |
The Prune property is true if decision trees are pruned and false if they are not. Pruning decision trees is not recommended for ensembles. The default value is false. See also classregtree.prune. |
SampleWithReplacement |
A logical flag specifying if data are sampled for each decision tree with replacement. True if TreeBagger samples data with replacement and false otherwise. True by default. |
Trees |
A cell array of size NTrees-by-1 containing the trees in the ensemble. See also NTrees. |
VarAssoc |
A matrix of size Nvars-by-Nvars with predictive measures of variable association, averaged across the entire ensemble of grown trees. If you grew the ensemble setting 'surrogate' to 'on', this matrix for each tree is filled with predictive measures of association averaged over the surrogate splits. If you grew the ensemble setting 'surrogate' to 'off' (default), VarAssoc is diagonal. |
VarNames |
A cell array containing the names of the predictor variables (features). TreeBagger takes these names from the optional 'names' parameter. The default names are 'x1', 'x2', etc. |
W |
Numeric vector of weights of length Nobs, where Nobs is the number of observations (rows) in the training data. TreeBagger uses these weights for growing every decision tree in the ensemble. The default W is ones(Nobs,1). |
X |
A numeric matrix of size Nobs-by-Nvars, where Nobs is the number of observations (rows) and Nvars is the number of variables (columns) in the training data. This matrix contains the predictor (or feature) values. |
Y |
An array of true class labels for classification, or response values for regression. Y can be a numeric column vector, a character matrix, or a cell array of strings. |
Value. To learn how this affects your use of the class, see Comparing Handle and Value Classes in the MATLAB Object-Oriented Programming documentation.
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