Class: ClassificationTree
Classification error by cross validation
returns the
cross-validated classification error (loss) for E
= cvloss(tree
)tree
, a
classification tree. The cvloss
method uses stratified partitioning
to create cross-validated sets. That is, for each fold, each partition of the data
has roughly the same class proportions as in the data used to train
tree
.
[___] = cvloss(
cross
validates with additional options specified by one or more tree
,Name,Value
)Name,Value
pair
arguments, using any of the previous syntaxes. You can specify several
name-value pair arguments in any order as Name1,Value1,…,NameN,ValueN
.
You can construct a cross-validated tree model with crossval
,
and call kfoldLoss
instead of cvloss
.
If you are going to examine the cross-validated tree more than once,
then the alternative can save time.
However, unlike cvloss
, kfoldLoss
does
not return SE
,Nleaf
, or BestLevel
. kfoldLoss
also
does not allow you to examine any error other than the classification
error.