Cross validate shrinking (pruning) ensemble
vals = cvshrink(ens)
[vals,nlearn]
= cvshrink(ens)
[vals,nlearn]
= cvshrink(ens,Name,Value)
returns
an vals
= cvshrink(ens
)L
byT
matrix with crossvalidated
values of the mean squared error. L
is the number
of lambda
values in the ens.Regularization
structure. T
is
the number of threshold
values on weak learner
weights. If ens
does not have a Regularization
property
filled in by the regularize
method, pass a lambda
namevalue
pair.
[
returns an vals
,nlearn
]
= cvshrink(ens
)L
byT
matrix
of the mean number of learners in the crossvalidated ensemble.
[
cross
validates with additional options specified by one or more vals
,nlearn
]
= cvshrink(ens
,Name,Value
)Name,Value
pair
arguments. You can specify several namevalue pair arguments in any
order as Name1,Value1,…,NameN,ValueN
.

A regression ensemble, created with 
Specify optional
commaseparated 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
.

A partition created with 

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 crossvalidated tree, a positive
integer. If you do not supply a crossvalidation method, Default: 

Vector of nonnegative regularization parameter values for lasso.
If empty, Default: 

Use leaveoneout cross validation by setting to 

Numeric vector with lower cutoffs on weights for weak learners. Default: 



