cvshrink
Class: ClassificationDiscriminant
Crossvalidate regularization of linear discriminant
Syntax
err = cvshrink(obj)
[err,gamma]
= cvshrink(obj)
[err,gamma,delta]
= cvshrink(obj)
[err,gamma,delta,numpred]
= cvshrink(obj)
[err,...] = cvshrink(obj,Name,Value)
Description
returns
a vector of crossvalidated classification error values for differing
values of the regularization parameter Gamma.err
= cvshrink(obj
)
[
also returns the vector
of Gamma values.err
,gamma
]
= cvshrink(obj
)
[
also returns the vector
of Delta values.err
,gamma
,delta
]
= cvshrink(obj
)
[
returns the vector
of number of nonzero predictors for each setting of the parameters
Gamma and Delta.err
,gamma
,delta
,numpred
]
= cvshrink(obj
)
[
cross
validates with additional options specified by one or more err
,...] = cvshrink(obj
,Name,Value
)Name,Value
pair
arguments.
Input Arguments

Discriminant analysis classifier, produced using 
NameValue Arguments
Specify optional pairs of arguments as
Name1=Value1,...,NameN=ValueN
, where Name
is
the argument name and Value
is the corresponding value.
Namevalue arguments must appear after other arguments, but the order of the
pairs does not matter.
Before R2021a, use commas to separate each name and value, and enclose
Name
in quotes.

Default: 

Vector of Gamma values for crossvalidation. Default: 

Number of Delta intervals for crossvalidation. For every value
of Gamma, Default: 

Number of Gamma intervals for crossvalidation. Default: 

Verbosity level, an integer from Default: 
Output Arguments

Numeric vector or matrix of errors.


Vector of Gamma values used for regularization. See Gamma and Delta. 

Vector or matrix of Delta values used for regularization. See Gamma and Delta.


Numeric vector or matrix containing the number of predictors
in the model at various regularizations.

Examples
More About
Tips
Examine the
err
andnumpred
outputs to see the tradeoff between crossvalidated error and number of predictors. When you find a satisfactory point, set the correspondinggamma
anddelta
properties in the model using dot notation. For example, if(i,j)
is the location of the satisfactory point, setobj.Gamma = gamma(i); obj.Delta = delta(i,j);