ClassificationPartitionedEnsemble
statistics: ClassificationPartitionedEnsemble
Cross-validated ensemble of decision trees for classification
A ClassificationPartitionedEnsemble object holds one ensemble per
fold of a partition, each fitted on the observations the fold keeps for
training, and answers for every observation with the ensemble of the fold
that held it out.
Create one with fitcensemble given a cross-validation option, or
with the crossval method of a ClassificationEnsemble or
ClassificationBaggedEnsemble.
See also: fitcensemble, ClassificationEnsemble.crossval, CompactClassificationEnsemble, cvpartition
Source Code: ClassificationPartitionedEnsemble
The ClassificationPartitionedEnsemble class contains the following properties:
A column cell array with one ClassificationEnsemble or
ClassificationBaggedEnsemble per fold, which resume
grows. This property is read-only.
A row vector. This property is read-only.
The Method of the ensemble, such as 'AdaBoostM1' or
'Bag'. This property is read-only.
This property is read-only.
Always empty. This property is read-only.
This property is read-only.
This property is read-only.
This property is read-only.
This property is read-only.
The weights of the ensemble that was cross-validated. The losses and edges weigh the held-out observations by them. This property is read-only.
A structure with the fields Type, Method,
'PartitionedEnsemble', LearnerTemplates, the
ModelParameters of the ensemble the folds were fitted as, and
NLearn, the number of folds. This property is read-only.
A column cell array with one CompactClassificationEnsemble per
fold. This property is read-only.
This property is read-only.
A cvpartition object. This property is read-only.
Always empty. This property is read-only.
This property is read-only.
This property is read-only.
This property is read-only.
The folds carry none; this one is applied once to the scores they return.
The ClassificationPartitionedEnsemble class offers the following public methods:
ClassificationPartitionedEnsemble: label = kfoldPredict (obj)
ClassificationPartitionedEnsemble: [label, scores] = kfoldPredict (obj)
scores holds each observation’s scores from the ensemble of the
fold that held it out, after ScoreTransform, and label
the class of highest score. An observation no fold held out, as under
a holdout partition, has NaN scores and the class of greatest
prior probability.
See also: ClassificationPartitionedEnsemble, ClassificationPartitionedEnsemble.kfoldLoss
ClassificationPartitionedEnsemble: L = kfoldLoss (obj)
ClassificationPartitionedEnsemble: L = kfoldLoss (…, name, value)
L is the loss of the out-of-fold scores, the held-out observations
weighted by W, those without scores left out.
Name-Value arguments:
| Name | Value | |
|---|---|---|
'Folds' | The folds to use, pooled. The default is all of them. | |
'LossFun' | A loss
CompactClassificationEnsemble.loss accepts. The default is
'classiferror'. | |
'Mode' | 'average' (default) for one loss
over the observations of every fold used, 'individual' for a
column with the loss of each fold, or 'cumulative' for a column
whose element t uses the first t learners of every fold. |
See also: ClassificationPartitionedEnsemble, ClassificationPartitionedEnsemble.kfoldEdge
ClassificationPartitionedEnsemble: e = kfoldEdge (obj)
ClassificationPartitionedEnsemble: e = kfoldEdge (…, name, value)
The weighted mean of the out-of-fold margins, weighted by W.
'Folds' and 'Mode' are taken as by kfoldLoss.
See also: ClassificationPartitionedEnsemble, ClassificationPartitionedEnsemble.kfoldMargin
ClassificationPartitionedEnsemble: m = kfoldMargin (obj)
m holds each observation’s margin under its out-of-fold scores,
NaN for one no fold held out.
See also: ClassificationPartitionedEnsemble, ClassificationPartitionedEnsemble.kfoldEdge
ClassificationPartitionedEnsemble: vals = kfoldfun (obj, fun)
fun is called once per fold as
fun (CMP, Xtrain, Ytrain, Wtrain, Xtest, Ytest, Wtest),
CMP being the fold’s compact ensemble and the weights those of
W, and must return a row. vals stacks the rows.
See also: ClassificationPartitionedEnsemble
ClassificationPartitionedEnsemble: CVMdl = resume (obj, NumLearningCycles)
ClassificationPartitionedEnsemble: CVMdl = resume (…, ’NPrint’, n)
Each fold’s ensemble is resumed, as ClassificationEnsemble.resume
does, by NumLearningCycles learners.
See also: ClassificationPartitionedEnsemble, ClassificationEnsemble.resume