RBF
RBF
is available in Forecasting and Decision Trees.
The RBF
procedure fits a radial basis function neural network,
which is a feedforward, supervised learning network with an input layer, a hidden layer called the
radial basis function layer, and an output layer. The hidden layer transforms the input vectors into
radial basis functions. Like the MLP
(multilayer perceptron) procedure, the
RBF
procedure performs prediction and classification.
RBF dependent variable [(MLEVEL = {S})] [dependent variable...]
{O}
{N}
[BY factor list] [WITH covariate list]
[/EXCEPT VARIABLES = varlist]
[/RESCALE [COVARIATE = {STANDARDIZED**}] [DEPENDENT = {STANDARDIZED**}]]
{NORMALIZED } {NORMALIZED }
{ADJNORMALIZED } {ADJNORMALIZED }
{NONE } {NONE }
[/PARTITION {TRAINING = {70** } TESTING = {30** } HOLDOUT = {0** }}]
{number} {number} {number}
{VARIABLE = varname }
[/ARCHITECTURE [{[MINUNITS = {AUTO** } MAXUNITS = {AUTO** }]}]
{integer} {integer}
{NUMUNITS = integer }
[HIDDENFUNCTION = {NRBF**}]]
{ORBF }
[/CRITERIA OVERLAP = {AUTO**}]
{number}
[/MISSING USERMISSING = {EXCLUDE**}]
{INCLUDE }
[/PRINT [CPS**] [NETWORKINFO**] [SUMMARY**] [CLASSIFICATION**]
[SOLUTION] [IMPORTANCE] [NONE]]
[/PLOT [NETWORK**] [PREDICTED] [RESIDUAL] [ROC]
[GAIN] [LIFT] [NONE]]
[/SAVE [PREDVAL[(varname [varname...])]]
[PSEUDOPROB[(rootname[:{25 }] [rootname...])]]]
{integer}
[/OUTFILE MODEL = 'file' ['file'...]]
** Default if the subcommand or keyword is omitted.
This command reads the active dataset and causes execution of any pending commands. See the topic Command Order for more information.
Syntax for
the RBF
command can be generated from the Radial Basis Function dialog.
Release History
Release 16.0
- Command introduced.
Example
RBF dep_var BY A B C WITH X Y Z.