Class: CompactRegressionEnsemble
Regression error
L = loss(ens,tbl,ResponseVarName)
L = loss(ens,tbl,Y)
L = loss(ens,X,Y)
L = loss(___,Name,Value)
returns
the mean squared error between the predictions of L
= loss(ens
,tbl
,ResponseVarName
)ens
to
the data in tbl
, compared to the true responses tbl.ResponseVarName
.
returns
the mean squared error between the predictions of L
= loss(ens
,tbl
,Y
)ens
to
the data in tbl
, compared to the true responses Y
.
returns
the mean squared error between the predictions of L
= loss(ens
,X
,Y
)ens
to
the data in X
, compared to the true responses Y
.
computes
the error in prediction with additional options specified by one or
more L
= loss(___,Name,Value
)Name,Value
pair arguments, using any of
the previous syntaxes.

A regression ensemble created with 

Sample data, specified as a table. Each row of If you trained 

Response variable name, specified as the name of a variable
in You must specify 

A matrix of predictor values. Each column of
If you trained 

A numeric column vector with the same number of rows as

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
.

Indices of weak learners in the ensemble ranging from Default: 

Function handle for loss function, or fun(Y,Yfit,W) where
The returned value Default: 

Meaning of the output
Default: 

A logical matrix of size Default: 

Numeric vector of observation weights with the same number of
elements as Default: 

Weighted mean squared error of predictions. The formula for 