RegressionEnsemble
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.
creates
an ensemble model that predicts responses to data. The ensemble consists
of models listed in ens
= fitrensemble(tbl
,ResponseVarName
,method
,nlearn
,learners
)learners
. For more information
on the syntax, see the fitrensemble
function
reference page.
creates
an ensemble model that predicts responses to data. The ensemble consists
of models listed in ens
= fitrensemble(tbl
,formula
,method
,nlearn
,learners
)learners
. For more information
on the syntax, see the fitrensemble
function
reference page.
creates
an ensemble model that predicts responses to data. The ensemble consists
of models listed in ens
= fitrensemble(tbl
,Y
,method
,nlearn
,learners
)learners
. For more information
on the syntax, see the fitrensemble
function
reference page.
ens = fitrensemble(X,Y,method,nlearn,learners)
returns
an ensemble model that can predict responses to data. The ensemble
consists of models listed in learners
. For more
information on the syntax, see the fitrensemble
function
reference page.
ens = fitrensemble(X,Y,method,nlearn,learners,Name,Value)
returns
an ensemble model with additional options specified by one or more Name,Value
pair
arguments. For more information on the syntax, see the fitrensemble
function reference page.

List of categorical predictors. 

A character vector describing how the ensemble combines learner predictions. 

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

A numeric array of fit information. The 

Character vector describing the meaning of the 

Cell array of character vectors 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, 

Description of the crossvalidation optimization of hyperparameters,
stored as a


A character vector 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 character vector describing the reason 

A structure containing the result of the 

A character vector with the name of the response variable 

Function handle for transforming scores, or character vector
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. 

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 
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 responses using ensemble of regression models 
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.
For an ensemble of regression trees, the Trained
property
contains a cell vector of ens.NumTrained
CompactRegressionTree
model
objects. For a textual or graphical display of tree t
in
the cell vector, enter
view(ens.Trained{t})
ClassificationEnsemble
 CompactRegressionEnsemble
 fitrensemble
 templateTree
 view