Package: classreg.learning.regr
Superclasses: CompactRegressionEnsemble
Ensemble regression
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.
Create a regression ensemble object using fitrensemble
.

Bin edges for numeric predictors, specified as a cell array of p numeric vectors, where p is the number of predictors. Each vector includes the bin edges for a numeric predictor. The element in the cell array for a categorical predictor is empty because the software does not bin categorical predictors. The software bins numeric predictors only if you specify the You can reproduce the binned predictor data X = mdl.X; % Predictor data
Xbinned = zeros(size(X));
edges = mdl.BinEdges;
% Find indices of binned predictors.
idxNumeric = find(~cellfun(@isempty,edges));
if iscolumn(idxNumeric)
idxNumeric = idxNumeric';
end
for j = idxNumeric
x = X(:,j);
% Convert x to array if x is a table.
if istable(x)
x = table2array(x);
end
% Group x into bins by using the Xbinned
contains the bin indices, ranging from 1 to the number of bins, for numeric predictors.
Xbinned values are 0 for categorical predictors. If
X contains NaN s, then the corresponding
Xbinned values are NaN s.


Categorical predictor
indices, specified as a vector of positive integers. 

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 or table 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 for regression ensemble 
removeLearners  Remove members of compact regression ensemble 
Value. To learn how value classes affect copy operations, see Copying Objects (MATLAB).
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
 plotPartialDependence
 templateTree
 view