Cross-validate naive Bayes classifier
returns a
cross-validated (partitioned) naive Bayes classifier (CVMdl
= crossval(Mdl
)CVMdl
)
from a trained naive Bayes classifier (Mdl
). By default,
crossval
uses 10-fold cross-validation on the training data
to create CVMdl
, a ClassificationPartitionedModel
classifier.
returns a partitioned naive Bayes classifier with additional options specified by
one or more name-value pair arguments. For example, you can specify the number of
folds or a holdout sample proportion.CVMdl
= crossval(Mdl
,Name,Value
)
Assess the predictive performance of Mdl
on
cross-validated data using the 'KFold'
name-value pair
argument and properties of CVMdl
, such as kfoldLoss
.
Return a partitioned naive Bayes classifier with stratified partitioning using
the name-value pair arguments 'KFold'
and
'Holdout'
.
Create a cvpartition
object cvp
using
cvp =
cvpartition
(n,'KFold',k)
. Return a
partitioned naive Bayes classifier with nonstratified partitioning using the
name-value pair 'CVPartition',cvp
.
Instead of first creating a naive Bayes classifier and then creating a cross-validation
classifier, you can create a cross-validated classifier directly by using fitcnb
and specifying any of these name-value pair arguments:
'CrossVal'
, 'CVPartition'
,
'Holdout'
, 'Leaveout'
, or
'KFold'
.
ClassificationNaiveBayes
| ClassificationPartitionedModel
| CompactClassificationNaiveBayes
| fitcnb
| kfoldLoss