Superclasses: RegressionEnsemble
Regression ensemble grown by resampling
RegressionBaggedEnsemble
combines a set of
trained weak learner models and data on which these learners were
trained. It can predict ensemble response for new data by aggregating
predictions from its weak learners.
ens = fitensemble(tbl,ResponseVarName,'bag',nlearn,learners,'type','regression')
creates
a bagged regression ensemble. For more information on the syntax,
see the fitensemble
function
reference page.
ens = fitensemble(tbl,formula,'bag',nlearn,learners,'type','regression')
creates
a bagged regression ensemble. For more information on the syntax,
see the fitensemble
function
reference page.
ens = fitensemble(tbl,Y,'bag',nlearn,learners,'type','regression')
creates
a bagged regression ensemble. For more information on the syntax,
see the fitensemble
function
reference page.
ens = fitensemble(X,Y,'bag',nlearn,learners,'type','regression')
creates
a bagged regression ensemble. For more information on the syntax,
see the fitensemble
function
reference page.

List of categorical predictors. 

A string describing how the ensemble combines learner predictions. 

Expanded predictor names, stored as a cell array of strings. If the model uses encoding for categorical variables, then 

A numeric array of fit information. The 

String describing the meaning of the 

A numeric scalar between 

Cell array of strings with names of the weak learners in the
ensemble. The name of each learner appears just once. For example,
if you have an ensemble of 100 trees, 

A string with the name of the algorithm 

Parameters used in training 

Numeric scalar containing the number of observations in the training data. 

Number of trained learners in the ensemble, a positive scalar. 

A cell array of names for the predictor variables, in the order
in which they appear in 

A string describing the reason 

A structure containing the result of the 

Boolean flag indicating if training data for weak learners in
this ensemble were sampled with replacement. 

A string with the name of the response variable 

Function handle for transforming scores, or string representing
a builtin transformation function. Add or change a ens.ResponseTransform = @function 

The trained learners, a cell array of compact regression models. 

A numeric vector of weights the ensemble assigns to its learners. The ensemble computes predicted response by aggregating weighted predictions from its learners. 

A logical matrix of size 

The scaled 

The matrix of predictor values that trained the ensemble. Each
column of 

The numeric column vector with the same number of rows as 
oobLoss  Outofbag regression error 
oobPredict  Predict outofbag response of ensemble 
compact  Create compact regression ensemble 
crossval  Cross validate ensemble 
cvshrink  Cross validate shrinking (pruning) ensemble 
regularize  Find weights to minimize resubstitution error plus penalty term 
resubLoss  Regression error by resubstitution 
resubPredict  Predict response of ensemble by resubstitution 
resume  Resume training ensemble 
shrink  Prune ensemble 
loss  Regression error 
predict  Predict response of ensemble 
predictorImportance  Estimates of predictor importance 
removeLearners  Remove members of compact regression ensemble 
Value. To learn how value classes affect copy operations, see Copying Objects in the MATLAB^{®} documentation.
Create a bagged regression ensemble to
predict the mileage of cars in the carsmall
data
set based on their engine displacement, horsepower, and weight:
load carsmall X = [Displacement Horsepower Weight]; ens = fitensemble(X,MPG,'bag',100,'Tree',... 'type','regression') ens = classreg.learning.regr.RegressionBaggedEnsemble: PredictorNames: {'x1' 'x2' 'x3'} CategoricalPredictors: [] ResponseName: 'Y' ResponseTransform: 'none' NumObservations: 94 NumTrained: 100 Method: 'Bag' LearnerNames: {'Tree'} ReasonForTermination: [1x77 char] FitInfo: [] FitInfoDescription: 'None' Regularization: [] FResample: 1 Replace: 1 UseObsForLearner: [94x100 logical]
Predict the mileage of a car whose characteristics are the average of those of the first 10 cars:
car10 = mean(X(1:10,:)); predict(ens,car10) ans = 14.6569