Class: RegressionEnsemble
Find weights to minimize resubstitution error plus penalty term
ens1 = regularize(ens)
ens1 = regularize(ens,Name,Value)
finds
optimal weights for learners in ens1
= regularize(ens
)ens
by lasso regularization. regularize
returns
a regression ensemble identical to ens
, but with
a populated Regularization
property.
computes
optimal weights with additional options specified by one or more ens1
= regularize(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 by 
Specify optional
commaseparated pairs of Name,Value
arguments. Name
is
the argument name and Value
is the corresponding value.
Name
must appear inside single quotes (' '
). You can
specify several name and value pair arguments in any order as
Name1,Value1,...,NameN,ValueN
.

Vector of nonnegative regularization parameter values for lasso.
For the default setting of Default: 

Maximum number of iterations allowed,
specified as a positive integer. If the algorithm
executes Default: 

Maximal number of passes for lasso optimization, a positive integer. Default: 

Relative tolerance on the regularized loss for lasso, a numeric positive scalar. Default: 

Verbosity level, either Default: 

A regression ensemble. Usually you set 