Class: ClassificationDiscriminant
Cross-validated discriminant analysis classifier
cvmodel = crossval(obj)
cvmodel = crossval(obj,Name,Value)
cvmodel = crossval(obj)obj, a fitted discriminant
analysis classifier. By default, crossval uses 10-fold
cross validation on the training data to create cvmodel.
cvmodel = crossval(obj,Name,Value)Name,Value pair
arguments.
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Discriminant analysis classifier, produced using |
Specify optional
comma-separated pairs of Name,Value arguments. Name is
the argument name and Value is the corresponding value.
Name must appear inside quotes. You can specify several name and value
pair arguments in any order as
Name1,Value1,...,NameN,ValueN.
|
Object of class Use only one of these options at a time: Default: |
|
Holdout validation tests the specified fraction of the data,
and uses the rest of the data for training. Specify a numeric scalar
from |
|
Number of folds to use in a cross-validated classifier, a positive integer value greater than 1. Use only one of these options at a time: Default: 10 |
|
Set to Use only one of these options at a time: |
Create a classification model for the Fisher iris data, and
then create a cross-validation model. Evaluate the quality the model
using kfoldLoss.
load fisheriris
obj = fitcdiscr(meas,species);
cvmodel = crossval(obj);
L = kfoldLoss(cvmodel)
L =
0.0200Assess the predictive performance of obj on
cross-validated data using the “kfold” methods and properties
of cvmodel, such as kfoldLoss.
You can create a cross-validation classifier directly from the
data, instead of creating a discriminant analysis classifier followed
by a cross-validation classifier. To do so, include one of these options
in fitcdiscr: 'CrossVal', 'CVPartition', 'Holdout', 'KFold',
or 'Leaveout'.
crossval | fitcdiscr | kfoldEdge | kfoldfun | kfoldLoss | kfoldMargin | kfoldPredict