ClassificationPartitionedEnsemble
Package: classreg.learning.partition
Superclasses: ClassificationPartitionedModel
Crossvalidated classification ensemble
Description
ClassificationPartitionedEnsemble
is a set of
classification ensembles trained on crossvalidated folds. Estimate the quality of
classification by cross validation using one or more “kfold” methods:
kfoldPredict
, kfoldLoss
, kfoldMargin
, kfoldEdge
, and kfoldfun
.
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
cvens = crossval(ens)
creates a crossvalidated ensemble from
ens
, a classification ensemble. For syntax details, see the
crossval
method reference page.
cvens = fitcensemble(X,Y,Name,Value)
creates a crossvalidated
ensemble when Name
is one of 'CrossVal'
,
'KFold'
, 'Holdout'
,
'Leaveout'
, or 'CVPartition'
. For syntax
details, see the fitcensemble
function reference
page.
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. 

List of the elements in 

Cell array of combiners across all folds. 

Square matrix, where 

Name of the crossvalidated model, a character vector. 

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

Object holding parameters of 

Number of data points used in training the ensemble, a positive integer. 

Number of weak learners used in training each fold of the ensemble, a positive integer. 

Partition of class 

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

Numeric vector of prior probabilities for each class. The order
of the elements of 

Name of the response variable 

Function handle for transforming scores, or character vector representing
a builtin transformation function. Add or change a ens.ScoreTransform = 'function' or ens.ScoreTransform = @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. 

Scaled 

A matrix or table of predictor values. Each column of 

Numeric vector, categorical vector, logical vector, character array, or
cell array of character vectors. Each row of 
Object Functions
gather  Gather properties of Statistics and Machine Learning Toolbox object from GPU 
kfoldEdge  Classification edge for crossvalidated classification model 
kfoldLoss  Classification loss for crossvalidated classification model 
kfoldMargin  Classification margins for crossvalidated classification model 
kfoldPredict  Classify observations in crossvalidated classification model 
kfoldfun  Crossvalidate function for classification 
resume  Resume training learners on crossvalidation folds 
Copy Semantics
Value. To learn how value classes affect copy operations, see Copying Objects.