RegressionBaggedEnsemble
Regression ensemble grown by resampling
Description
RegressionBaggedEnsemble combines a set of trained weak
learner models and the data on which the learners were trained. Use the
predict object function to predict the ensemble response for
new data by aggregating predictions from the weak learners.
Creation
Create a bagged regression ensemble object using fitrensemble. Set the name-value argument Method of
fitrensemble to "Bag" to use bootstrap
aggregation, or bagging (for example, random forest).
For a description of bagged regression ensembles, see Bootstrap Aggregation (Bagging) and Random Forest.
Properties
Object Functions
compact | Reduce size of machine learning model |
crossval | Cross-validate machine learning model |
cvshrink | Cross-validate pruning and regularization of regression ensemble |
gather | Gather properties of Statistics and Machine Learning Toolbox object from GPU |
lime | Local interpretable model-agnostic explanations (LIME) |
loss | Regression error for regression ensemble model |
oobLoss | Out-of-bag error for bagged regression ensemble model |
oobPermutedPredictorImportance | Out-of-bag predictor importance estimates for random forest of regression trees by permutation |
oobPredict | Predict out-of-bag responses of bagged regression ensemble |
partialDependence | Compute partial dependence |
plotPartialDependence | Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots |
predict | Predict responses using regression ensemble model |
predictorImportance | Estimates of predictor importance for regression ensemble of decision trees |
regularize | Find optimal weights for learners in regression ensemble |
resubLoss | Resubstitution loss for regression ensemble model |
resubPredict | Predict response of regression ensemble by resubstitution |
resume | Resume training of regression ensemble model |
shapley | Shapley values |
shrink | Prune regression ensemble |
Examples
Load the carsmall data set. Consider a model that explains a car's fuel economy (MPG) using its weight (Weight) and number of cylinders (Cylinders).
load carsmall
X = [Weight Cylinders];
Y = MPG;Train a bagged ensemble of 100 regression trees using all measurements.
Mdl = fitrensemble(X,Y,Method="Bag")Mdl =
RegressionBaggedEnsemble
ResponseName: 'Y'
CategoricalPredictors: []
ResponseTransform: 'none'
NumObservations: 94
NumTrained: 100
Method: 'Bag'
LearnerNames: {'Tree'}
ReasonForTermination: 'Terminated normally after completing the requested number of training cycles.'
FitInfo: []
FitInfoDescription: 'None'
Regularization: []
FResample: 1
Replace: 1
UseObsForLearner: [94×100 logical]
Properties, Methods
Mdl is a RegressionBaggedEnsemble model object.
Mdl.Trained is the property that stores a 100-by-1 cell vector of the trained, compact regression trees (CompactRegressionTree model objects) that compose the ensemble.
Plot a graph of the first trained regression tree.
view(Mdl.Trained{1},Mode="graph")
By default, fitrensemble grows deep trees for bags of trees.
Estimate the in-sample mean-squared error (MSE).
L = resubLoss(Mdl)
L = 12.4048
Tips
For a bagged ensemble of regression trees Mdl, the
Trained property of Mdl stores a cell vector
of Mdl.NumTrained
CompactRegressionTree model objects. For a textual or graphical display of
tree t in the cell vector,
enter
view(Mdl.Trained{t})Alternative Functionality
Bootstrap Aggregation Methods
For classification or regression, you can choose two approaches for bagging:
Classification: create a bagged ensemble using
fitcensembleorTreeBagger.Regression: create a bagged ensemble using
fitrensembleorTreeBagger.
For help choosing between these approaches, see Ensemble Algorithms and Suggestions for Choosing an Appropriate Ensemble Algorithm.
Extended Capabilities
Version History
Introduced in R2011a
