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 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
