RegressionPartitionedEnsemble
Package: classreg.learning.partition
Superclasses: RegressionPartitionedModel
Crossvalidated regression ensemble
Description
RegressionPartitionedEnsemble
is a set of regression
ensembles trained on crossvalidated folds. Estimate the quality of classification by cross
validation using one or more “kfold” methods: kfoldfun
, kfoldLoss
, or kfoldPredict
. Every “kfold” method uses models trained on infold
observations to predict response for outoffold observations. For example, suppose you cross
validate using five folds. In this case, every training fold contains roughly 4/5 of the data
and every test fold contains roughly 1/5 of the data. The first model stored in
Trained{1}
was trained on X
and Y
with the first 1/5 excluded, the second model stored in Trained{2}
was
trained on X
and Y
with the second 1/5 excluded, and so
on. When you call kfoldPredict
, it computes predictions for the first
1/5 of the data using the first model, for the second 1/5 of data using the second model and
so on. In short, response for every observation is computed by kfoldPredict
using the model trained without this observation.
Construction
creates a crossvalidated ensemble from cvens
=
crossval(ens
)ens
, a regression ensemble. For
syntax details, see the crossval
method reference page.
creates a
crossvalidated ensemble when cvens
= fitrensemble(X,Y,Name,Value)Name
is one of 'crossval'
,
'kfold'
, 'holdout'
, 'leaveout'
, or
'cvpartition'
. For syntax details, see the fitrensemble
function reference page.
Input Arguments

A regression ensemble constructed with 
Properties

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. 

Name of the crossvalidated model, a character vector. 

Number of folds used in a crossvalidated tree, a positive integer. 

Object holding parameters of 

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

Vector of 

The partition of class 

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

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 

Cell array of ensembles trained on crossvalidation folds. Every ensemble is full, meaning it contains its training data and weights. 

Cell array of compact ensembles trained on crossvalidation folds. 

The scaled 

A matrix or table of predictor values. Each column of 

A numeric column vector with the same number of rows as 
Object Functions
gather  Gather properties of Statistics and Machine Learning Toolbox object from GPU 
kfoldLoss  Loss for crossvalidated partitioned regression model 
kfoldPredict  Predict responses for observations in crossvalidated regression model 
kfoldfun  Crossvalidate function for regression 
resume  Resume training ensemble 
Copy Semantics
Value. To learn how value classes affect copy operations, see Copying Objects.