ClassificationPartitionedEnsemble
Cross-validated classification ensemble
Description
ClassificationPartitionedEnsemble
is a set
of classification ensembles trained on cross-validated 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 in-fold observations to
predict response for out-of-fold 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.
Creation
Description
cvens = crossval(ens)
creates a cross-validated ensemble
from ens
, a classification ensemble. For syntax details, see
the crossval
method reference page.
cvens = fitcensemble(X,Y,Name,Value)
creates a
cross-validated ensemble when Name
is one of
'CrossVal'
, 'KFold'
,
'Holdout'
, 'Leaveout'
, or
'CVPartition'
. For syntax details, see the fitcensemble
function reference
page.
Properties
Object Functions
gather | Gather properties of Statistics and Machine Learning Toolbox object from GPU |
kfoldEdge | Classification edge for cross-validated classification model |
kfoldLoss | Classification loss for cross-validated classification model |
kfoldMargin | Classification margins for cross-validated classification model |
kfoldPredict | Classify observations in cross-validated classification model |
kfoldfun | Cross-validate function for classification |
resume | Resume training of cross-validated classification ensemble model |