5 NO_BB_HU -0.097288 5.56801E-6 NO_BB -> HU1
6 _DUP2_ -0.159583 2.38931E-6 DIVISIONEAST -> HU1
7 BIAS_HU1 4.100399 1.23564E-6 BIAS -> HU1
8 _DUP3_ 0.114473 -4.1311E-8 CR_HITS -> LOGSALAR
9 _DUP4_ 0.186717 5.68816E-9 NO_HITS -> LOGSALAR
10 _DUP5_ 0.156385 1.26352E-9 NO_OUTS -> LOGSALAR
11 _DUP6_ -0.042475 4.3267E-8 NO_ERROR -> LOGSALAR
12 NO_BB_LO 0.151513 2.33439E-9 NO_BB -> LOGSALAR
13 _DUP7_ 0.055178 1.6828E-8 DIVISIONEAST -> LOGSALAR
14 HU1_LOGS 0.839144 -4.7461E-8 HU1 -> LOGSALAR
15 BIAS_LOG 5.490961 3.77028E-8 BIAS -> LOGSALAR
Value of Objective Function = 0.1453574605
List Report of Selected Variables in the Score OUTFIT= Data Set
The example PROC PRINT report of the OUTFIT= data set contains selected summary statistics from the scored training data set.
Partial Listing of the Score OUTFIT= Data Set
Test: Mean Test: Root Mean Test: Maximum
_ITER_ _PNAME_ Squared Error. Squared Error. Absolute Error.
0 P_LOGSAL 0.15595 0.39491 1.60237
Diagnostic Plots for the Scored Test Baseball Data
Plot of the log of salary versus the predicted log of salary.
Plot of the residual values versus the predicted log of salary.
Copyright 2000 by SAS Institute Inc., Cary, NC, USA. All rights reserved.
The preliminary PROC DMREG run selects the reduced input set.
proc dmreg data=sampsio.dmdbase dmdbcat=sampsio.dmdbase
testdata=sampsio.dmtbase outest=regest;
class league division position;
model logsalar = no_atbat no_hits no_home no_runs no_rbi no_bb
yr_major cr_atbat cr_hits cr_home cr_runs
cr_rbi cr_bb league division position no_outs
no_assts no_error /
error=normal selection=stepwise
slentry=0.25 slstay=0.25 choose=sbc;
title1 'Preliminary DMDREG Stepwise Selection';
run;
The PROC NEURAL statement invokes the procedure. The DATA= option identifies
the training data set that is used to fit the model. The DMDBCAT= option identifies
the training catalog. The RANDOM= option specifies the seed that is used to
set the random initial weights.
proc neural data=sampsio.dmdbase
dmdbcat=sampsio.dmdbase
random=12345;
The INPUT statements specifies the input layers. There are separate
input layers for the interval and nominal inputs. The LEVEL= option specifies
the measurement level. The ID= option specifies an identifier for each input
layer.
input cr_hits no_hits no_outs no_error no_bb
/ level=interval id=int;
input division / level=nominal id=nom;
The HIDDEN statement sets the number of hidden units. The ID= option
specifies an identifier for the hidden layer. By default, the combination
function is set to linear and the activation function is set to hyperbolic
tangent.
hidden 1 / id=hu;
The TARGET statement defines an output layer. The output layer computes
predicted values and compares those predicted values with the value of the
target variable (LOGSALAR). The LEVEL= option specifies the target measurement
level. The ID= option specifies an identifier for the output layer. By default,
the combination function is set to linear, the activation function is set
to the identity, and the error function is set to normal for continuous targets.
target logsalar /
level=interval
id=tar ;
The CONNECT statements specify how to connect the layers. The id-list
specifies the identifier of two or more layers to connect. In this example,
each input unit is connected to the hidden unit and to the output unit, and
the hidden unit is connected to the output unit.
connect int tar;
connect nom tar;
connect int hu;
connect nom hu;
connect hu tar;
The PRELIM statement does preliminary training using 10 different sets
of initial weights. The weights from the preliminary run with the smallest
objective function among all runs are retained for subsequent training when
using the TRAIN statement. Preliminary training may help prevent the network
from converging to a bad local minima.
prelim 10;
The TRAIN statement trains the network in order to find the best weights
(parameter estimates) to fit the training data. By default, the Levenberg-Marquardt
optimization technique is used for small least squares networks, such as the
one in this example.
train;
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